CN116566179A - Model prediction control method for three-phase three-level T-type grid-connected inverter - Google Patents
Model prediction control method for three-phase three-level T-type grid-connected inverter Download PDFInfo
- Publication number
- CN116566179A CN116566179A CN202310439614.9A CN202310439614A CN116566179A CN 116566179 A CN116566179 A CN 116566179A CN 202310439614 A CN202310439614 A CN 202310439614A CN 116566179 A CN116566179 A CN 116566179A
- Authority
- CN
- China
- Prior art keywords
- current
- phase
- voltage
- grid
- level
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 35
- 239000013598 vector Substances 0.000 claims abstract description 68
- 230000009466 transformation Effects 0.000 claims abstract description 13
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 238000005070 sampling Methods 0.000 claims abstract description 11
- 230000005855 radiation Effects 0.000 claims abstract description 10
- 230000008859 change Effects 0.000 claims description 4
- 230000001131 transforming effect Effects 0.000 claims description 2
- 230000007935 neutral effect Effects 0.000 abstract description 16
- 238000005457 optimization Methods 0.000 abstract description 8
- 230000008901 benefit Effects 0.000 abstract description 6
- 238000004422 calculation algorithm Methods 0.000 description 26
- 238000004088 simulation Methods 0.000 description 19
- 230000006870 function Effects 0.000 description 15
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000011217 control strategy Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M1/00—Details of apparatus for conversion
- H02M1/08—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters
- H02M1/088—Circuits specially adapted for the generation of control voltages for semiconductor devices incorporated in static converters for the simultaneous control of series or parallel connected semiconductor devices
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02M—APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF
- H02M1/00—Details of apparatus for conversion
- H02M1/12—Arrangements for reducing harmonics from ac input or output
-
- 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
- H02M7/483—Converters with outputs that each can have more than two voltages levels
- H02M7/4833—Capacitor voltage balancing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Inverter Devices (AREA)
Abstract
A model predictive control method of a three-phase three-level T-type grid-connected inverter comprises the following steps: sampling the current and the voltage of a power grid, and carrying out alpha beta coordinate transformation on the current and the voltage through Clarke transformation; combining with Euler formula to obtain predicted current, and calculating cost function according to predicted current and reference value thereof; comparing which middle voltage vector in the 6 sectors of the vector radiation range can minimize the value of the cost function, and selecting the first sector where the corresponding middle voltage vector is located for next calculation; and calculating the predicted current again, and then applying the voltage vectors in all the sub-sectors in the first sector to calculate a cost function, and comparing to obtain an optimal value. The invention has the advantages that: the total harmonic distortion of the current can be reduced by the sector optimization selection and the application of the combined voltage vector, and the neutral point potential balance is promoted. The calculated amount can be obviously reduced by a two-step judging method.
Description
Technical Field
The invention belongs to the technical field of power electronics, and particularly relates to a model predictive control method of a three-phase three-level T-type grid-connected inverter.
Background
The grid-connected inverter is used as equipment for directly connecting a direct-current generator with a power grid, and plays a vital role in the whole power grid system. The three-phase three-level T-shaped circuit topological structure has been widely focused and applied in recent years. The circuit has a relatively simple structure and high efficiency under the condition of medium and small power. Common inverter control methods are Proportional Integral (PI) control, repetitive control, and Proportional Resonance (PR) control. However, such linear control uses a simplified system linearization model, sometimes cannot accurately describe the characteristics of the nonlinear system, and when the nonlinear system is used in a power electronic inverter, the interference resistance of the system is poor. Once the robustness is low, the predetermined performance requirements will not be met. And the model predictive control can be well adapted to the characteristics of a nonlinear system. Meanwhile, the finite control set model predictive control has the advantages of quick transient response, simple realization, direct processing of nonlinear constraint and the like, and is widely applied to various power electronic converters at present. The finite control set model predictive control consists of a predictive model, an objective function and rolling optimization. The method can carry out cyclic calculation and prediction under the limited switch state, and all switch states can be calculated on line, so that the method has good adaptability and stability. And it does not need PWM modulation module, making system control easier to implement.
At present, a voltage vector enumeration method is generally adopted in a traditional finite control set model prediction control strategy, and this results in higher calculation load and increases the calculation load of a microprocessor. In addition, the single-vector output method does not sufficiently consider current tracking and neutral point potential balance, and current ripple and neutral point potential deviation are likely to occur.
There are techniques to reduce the computational load by reducing the control set. The scheme takes the voltage vector at the previous moment and the voltage vector adjacent to the voltage vector at the previous moment as a control set at the current moment, and reduces the candidate vectors from 125 to 7. However, the optimal vector selected by the method may not be in the control set at the current moment, and the control performance of the inverter is reduced. At the same time, the scheme promotes the neutral point potential balance by controlling the acting time of the small vectors.
The traditional finite control set model predictive control strategy requires enumeration calculation of 27 voltage vectors, and generates a larger calculation load. The single-vector output method does not sufficiently consider current tracking and neutral point potential balance, and current ripple and neutral point potential deviation are liable to occur.
Disclosure of Invention
The aim of the invention is achieved by the following technical scheme.
A model predictive control method of a three-phase three-level T-type grid-connected inverter comprises the following steps:
sampling the current and the voltage of a power grid, and carrying out alpha beta coordinate transformation on the current and the voltage through Clarke transformation;
combining with Euler formula to obtain predicted current, and calculating cost function according to predicted current and reference value thereof;
comparing which middle voltage vector in the 6 sectors of the vector radiation range can minimize the value of the cost function, and selecting the first sector where the corresponding middle voltage vector is located for next calculation;
and calculating the predicted current again, calculating the cost function by applying the voltage vectors in all the sub-sectors in the first sector, comparing which voltage vector can minimize the value of the cost function to obtain an optimal voltage vector, and applying the switching state corresponding to the optimal voltage vector to the switching device of the inverter.
Further, the three-phase three-level T-type grid-connected inverter comprises three T-type NPC flat bridges.
Further, according to kirchhoff's voltage law, the output phase voltage of the inverter is expressed as:
wherein Rs is line resistance, ls is filter inductance, e a 、e b 、e c For three-phase network voltage, i a 、i b 、i c U is three-phase grid-connected current flowing to the power grid through the filter inductance Ls and the line resistance Rs aN 、u bN 、u cN Three-phase voltages are output for the inverter.
Further, said transforming it with Clarke transformation of α - β coordinates comprises:
clarke transformation is carried out on the formula (1) to obtain a mathematical expression under an alpha-beta coordinate system:
i α ,i β u is the current in the alpha-beta coordinate system α 、u β For voltages in the alpha-beta coordinate system, e α 、e β Is an output alternating current signal in an alpha-beta coordinate system.
Further, the combining the euler equation to obtain the predicted current includes:
current i α ,i β The rate of change over a sampling period can be approximated by euler:
where k is the number of samples, T S Is the sampling period;
from this, the current prediction value can be obtained:
further, the cost function is expressed as:
wherein , and />For a current reference value, i, given in the αβ coordinate system at time k+1 α (k+1) and i β (k+1) is a current predicted value in the αβ coordinate system at time k+1.
Further, the 6 sectors are divided based on an effective radiation range of a voltage vector, which is a region where a linear distance from a reference vector to a given vector is shortest.
The invention has the advantages that: the total harmonic distortion of the current can be reduced by the sector optimization selection and the application of the combined voltage vector, and the neutral point potential balance is promoted. The calculated amount can be obviously reduced by a two-step judging method.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 shows a circuit topology diagram of a three-phase three-level T-grid-connected inverter according to an embodiment of the present invention.
Fig. 2 shows a schematic diagram of vector radiation range and sector division.
FIG. 3 shows a flow chart of a modified FCS-MPC algorithm.
Fig. 4 shows a simulation diagram of the output current under a conventional algorithm.
Fig. 5 shows a simulation diagram of a-phase voltage and output current under a conventional algorithm.
FIG. 6 shows i under a conventional algorithm a Is a simulation of the total harmonic distortion of (a).
Fig. 7 shows a simulation of the output current under the improved algorithm.
Fig. 8 shows a simulation of a-phase voltage and output current for the improved algorithm.
FIG. 9 shows i under the modified algorithm a Is a simulation of the total harmonic distortion of (a).
Fig. 10 shows a simulation of the output current without the improved algorithm of the two-step decision.
FIG. 11 shows i under an improved algorithm without two-step determination a Is a simulation of the total harmonic distortion of (a).
FIG. 12 shows the neutral point potential V under the conventional algorithm p ,V n And the difference thereof.
FIG. 13 shows a middle stage under a modified algorithmSex point potential V p ,V n And the difference thereof.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Term interpretation:
three-level inverter: four power semiconductor devices are arranged on bridge arms of the three-level inverter, and the three-level inverter realizes multi-level step wave output voltage through different combinations of voltage division and switching actions on a direct current side, so that the waveform is more similar to a sine wave.
Finite control set model predictive control: the basic idea of the finite control set model predictive control is to divide the system state into a finite plurality of sets, then predict the likelihood of future states based on the information of the current state and the historical state, and control based on the prediction result. The advantage of this approach is that non-linear, time-varying and uncertain systems can be handled while multiple objectives and constraints can be considered.
Total Harmonic Distortion (THD): the total harmonic distortion indicates that when the power amplifier works, due to the secondary generated by unavoidable oscillation or other resonance of the circuit, the third harmonic is superposed with the actual input signal, the signal output at the output end is not simply the same component as the input signal, but the signal containing harmonic components, and the comparison of the redundant harmonic components with the actual input signal is expressed as the total harmonic distortion by percentage.
Neutral point potential balance: in the inverter, since there is an unbalanced dc side neutral point potential, the switching devices are subjected to different voltages, and the devices are damaged seriously, and in addition, harmonics occur in the process, the output performance of the inverter is affected, so that the dc side neutral point potential balance needs to be controlled.
The invention applies sector optimization selection in model prediction control of a three-phase three-level T-type grid-connected inverter, divides sectors based on the effective radiation range of voltage vectors, and uses combined voltage vectors to replace traditional small and medium voltage vectors which can affect midpoint potential. Based on the method, a two-step judgment method based on the middle voltage vector judgment of the large sector is applied.
The circuit topology of the three-phase three-level T-type grid-connected inverter used in the invention is shown in figure 1.
The T-inverter topology comprises three T-NPC flat bridges, where S ax ,S bx ,S cx (x=1 to 4) represents four switches of the arms a, b, c. Taking bridge A as an example, when switch S a1 ,S a2 Conduction and switch S a3 ,S a4 When turned off, the output voltage is U dc And/2, the output voltage is independent of the current direction, and the output state is defined as 'P'. When the switch S a2 ,S a3 Conduction and switch S a1 ,S a4 When the power-off is turned off, the output voltage is 0, the output voltage is irrelevant to the current direction, and the output state is defined as 'O'. When the switch S a3 ,S a4 Conduction and switch S a1 ,S a2 When turned off, the output voltage is-U dc And/2, the output voltage of the bridge is independent of the current direction, and the output state is defined as 'N'. The output states of the three-phase three-level T-type inverter are shown in table 1.
TABLE 1 output state of three-phase three-level T-grid inverter
The mathematical model of the system used in the invention is shown in the following formula.
According to kirchhoff's voltage law, the output phase voltage of an inverter can be expressed as:
wherein Rs is line resistance, ls is filter inductance, e a 、e b 、e c For three-phase network voltage, i a 、i b 、i c U is three-phase grid-connected current flowing to the power grid through the filter inductance Ls and the line resistance Rs aN 、u bN 、u cN Three-phase voltages are output for the inverter.
Clarke transformation is carried out on the above formula, and a mathematical expression under an alpha-beta coordinate system can be obtained:
current i α ,i β The rate of change over a sampling period can be approximated by euler:
from this, the current prediction value can be obtained:
and then judging the predicted value of the current by using the cost function, so as to select the optimal switch state combination to control the switch tube. The cost function can be expressed as:
in the expression of this, the expression "a" is used, and />For a current reference value, i, given in the αβ coordinate system at time k+1 α (k+1) and i β (k+1) is at the alpha beta coordinate at the time of k+1The current predicted value is used.
The present invention utilizes the effective radiation range based on voltage vectors to divide sectors. The effective radiation range of the voltage vector is the region where the linear distance from the reference vector to the given vector is shortest. As shown in fig. 2 below, the effective radiation range is each polygonal area surrounded by a broken line, dividing the entire large sector into six sectors.
In the conventional voltage vector selection process, both the small voltage vector and the medium voltage vector have an effect on the neutral point voltage, and thus these voltage vectors are replaced with vector combinations. Taking sector I as an example, the combined vectors are shown in table 2.
Table 2 vector combinations in sector I
| Sub-sector | V(k) |
| A | V 26 |
| B | 1/2V 13 +1/2V 14 |
| C | 1/2V 15 +1/2V 16 |
| D | V 1 |
| E | 1/2V 1 +1/2V 2 |
| F | V 2 |
In order to reduce the calculation amount, a method based on a medium voltage vector is proposed, and then the voltage vector optimization is performed in a selected large sector. Thus, only one third of the calculation is needed to achieve the same performance as the conventional method. For example, if the target voltage vector V is selected 7 If the calculated cost function value is the smallest, a sector I should be selected, and then each voltage vector in the sector is calculated and optimized.
The flow chart of the improved finite control set model predictive control is shown in fig. 3 below.
Firstly, sampling current and voltage of a power grid, carrying out alpha beta coordinate transformation on the current and the voltage through Clarke transformation, obtaining a predicted current by combining with an Euler formula, and calculating a cost function according to the predicted current and a reference value thereof. Comparing which middle voltage vector in the 6 sectors can minimize the value of the cost function, and selecting the sector where the corresponding middle voltage vector is located for the next calculation. And calculating the predicted current again, calculating the cost function by applying the voltage vectors in all the sub-sectors in the first sector, comparing which voltage vector can minimize the value of the cost function to obtain an optimal voltage vector, and applying the switching state corresponding to the optimal voltage vector to the switching device of the inverter. Assuming sector i is selected, the predicted current is calculated as well, then the cost function is calculated by applying the voltage vectors in all the sub-sectors in sector i, the optimal value is obtained by comparison, then the next sampling time is waited, and the algorithm is repeated. Similarly, the algorithm for the other sectors remains the same. (V) 1 ～V 6 Is defined as a large voltage vector, V 7 ～V 12 Is defined as a medium voltage vector, V 13 ～V 24 Is defined as a small voltage vector, V 25 ～V 27 Defined as a zero voltage vector).
Specific examples:
simulation verification is performed below, and specific simulation parameters are shown in table 3.
TABLE 3 simulation parameters
| Parameters (parameters) | Type(s) | Numerical value |
| U dc | DC bus voltage | 800V |
| i ref | Reference current | 20～30A |
| C 1 ,C 2 | DC side capacitor | 50μF |
| L S | AC side inductor | 10mH |
| R S | Internal resistance of inductor | 0.05Ω |
| T S | Sampling time | 50μs |
Firstly, the traditional FCS-MPC algorithm and the improved FCS-MPC algorithm added with sector optimization and two-step judgment are respectively subjected to simulation verification, the reference current is changed from 20A to 30A in a sudden change manner in 0.05s, the steady state performance and the dynamic performance of the traditional FCS-MPC algorithm and the improved FCS-MPC algorithm added with sector optimization and two-step judgment can be simultaneously compared, and the total harmonic distortion of the traditional FCS-MPC algorithm and the improved FCS-MPC algorithm can be analyzed and compared.
And secondly, the simulation experiments before and after the two-step judging method is added are compared, so that the algorithm performance is proved not to be influenced, and the rigor and the accuracy of the experiments are ensured.
Finally, the influence of the algorithm before and after improvement on the neutral point potential balance is compared through simulation analysis.
Experimental Effect of the invention
Fig. 4 and 6 are simulation results of the output current and the total harmonic distortion under the conventional FCS-MPC strategy, and fig. 7 and 9 are simulation results of the output current and the total harmonic distortion under the proposed FCS-MPC strategy. Fig. 5 and 8 are the grid a-phase voltage and output current under two strategies. Under two control strategies, the phase angle of the grid phase voltage a is consistent with that of the output current, and the effectiveness of simulation is verified.
Simulation results show that when the reference current is changed from 20A to 30A, the output current under both strategies can keep good tracking performance and shorter response time. The output current after the mutation of the two strategies is stable within 0.001s, and the dynamic performance is good. However, as can be seen from the graph, compared with the traditional strategy, the total harmonic distortion of the output current is smaller and the current quality is better under the strategy of the invention.
In addition, to ensure that the secondary judgment of the sector in the improved FCS-MPC algorithm does not affect the optimization of the voltage vector and the quality of the output current in the whole system, simulation analysis was also performed on the algorithm without using the sector, as shown in fig. 10 and 11 above. Since the total harmonic distortion values are the same, it can be inferred that the secondary sector judgment does not affect the performance of the output current.
Fig. 12 and 13 are neutral point potential V under conventional algorithm and modified algorithm, respectively p ,V n And the difference thereof. It can be seen that while conventional algorithms can achieve neutral point potential balance, the present invention is optimized by sector and voltageVector combination, can make V p and Vn And the values of (2) are closer together, thereby stabilizing the neutral point potential balance.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, the present application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and the above description of specific languages is provided for disclosure of preferred embodiments of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed application requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the present application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functions of some or all of the components in a virtual machine creation system according to embodiments of the present application may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present application may also be embodied as a device or system program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present application may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (7)
1. The model prediction control method of the three-phase three-level T-type grid-connected inverter is characterized by comprising the following steps of:
sampling the current and the voltage of a power grid, and carrying out alpha beta coordinate transformation on the current and the voltage through Clarke transformation;
combining with Euler formula to obtain predicted current, and calculating cost function according to predicted current and reference value thereof;
comparing which middle voltage vector in the 6 sectors of the vector radiation range can minimize the value of the cost function, and selecting the first sector where the corresponding middle voltage vector is located for next calculation;
and calculating the predicted current again, calculating the cost function by applying the voltage vectors in all the sub-sectors in the first sector, comparing which voltage vector can minimize the value of the cost function to obtain an optimal voltage vector, and applying the switching state corresponding to the optimal voltage vector to the switching device of the inverter.
2. The model predictive control method of a three-phase three-level T-grid-connected inverter of claim 1, wherein,
the three-phase three-level T-shaped grid-connected inverter comprises three T-shaped NPC flat bridges.
3. The model predictive control method of a three-phase three-level T-type grid-connected inverter according to claim 1 or 2, characterized in that,
according to kirchhoff's voltage law, the output phase voltage of the inverter is expressed as:
wherein Rs is line resistance, ls is filter inductance, e a 、e b 、e c For three-phase network voltage, i a 、i b 、i c U is three-phase grid-connected current flowing to the power grid through the filter inductance Ls and the line resistance Rs aN 、u bN 、u cN Three-phase voltages are output for the inverter.
4. The model predictive control method of a three-phase three-level T-grid-connected inverter of claim 3, wherein,
said transforming it by Clarke transformation into αβ coordinates comprises:
clarke transformation is carried out on the formula (1) to obtain a mathematical expression under an alpha-beta coordinate system:
i α ,i β u is the current in the alpha-beta coordinate system α 、u β For voltages in the alpha-beta coordinate system, e α 、e β Is an output alternating current signal in an alpha-beta coordinate system.
5. The model predictive control method of a three-phase three-level T-grid-connected inverter of claim 4, wherein,
the combining Euler formula to obtain the predicted current includes:
current i α ,i β The rate of change over a sampling period can be approximated by euler:
where k is the number of samples, T S Is the sampling period;
from this, the current prediction value can be obtained:
6. the model predictive control method of a three-phase three-level T-grid-connected inverter of claim 5, wherein,
the cost function is expressed as:
wherein , and />For a current reference value, i, given in the αβ coordinate system at time k+1 α (k+1) and i β (k+1) is a current predicted value in the αβ coordinate system at time k+1.
7. The model predictive control method of a three-phase three-level T-grid-connected inverter of claim 1, wherein,
the 6 sectors are divided based on the effective radiation range of the voltage vector, which is the area where the linear distance from the reference vector to the given vector is the shortest.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310439614.9A CN116566179A (en) | 2023-04-23 | 2023-04-23 | Model prediction control method for three-phase three-level T-type grid-connected inverter |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310439614.9A CN116566179A (en) | 2023-04-23 | 2023-04-23 | Model prediction control method for three-phase three-level T-type grid-connected inverter |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116566179A true CN116566179A (en) | 2023-08-08 |
Family
ID=87495666
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310439614.9A Pending CN116566179A (en) | 2023-04-23 | 2023-04-23 | Model prediction control method for three-phase three-level T-type grid-connected inverter |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116566179A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117614300A (en) * | 2024-01-23 | 2024-02-27 | 苏州大学 | Continuous model predictive control method for T-type three-level three-phase four-bridge arm inverter |
-
2023
- 2023-04-23 CN CN202310439614.9A patent/CN116566179A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117614300A (en) * | 2024-01-23 | 2024-02-27 | 苏州大学 | Continuous model predictive control method for T-type three-level three-phase four-bridge arm inverter |
| CN117614300B (en) * | 2024-01-23 | 2024-04-05 | 苏州大学 | Continuous model predictive control method for T-type three-level three-phase four-bridge arm inverter |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Machado et al. | A neural network-based dynamic cost function for the implementation of a predictive current controller | |
| Lee et al. | An improved finite-set model predictive control based on discrete space vector modulation methods for grid-connected three-level voltage source inverter | |
| Kakosimos et al. | Predictive control of a grid-tied cascaded full-bridge NPC inverter for reducing high-frequency common-mode voltage components | |
| Nishida et al. | Current control implementation with deadbeat algorithm for three-phase current-source active power filter | |
| CN111525572B (en) | Method, device, equipment and storage medium for determining power quality grade in power grid | |
| Nguyen et al. | Simplified indirect model predictive control method for a modular multilevel converter | |
| CN112003491B (en) | Control method and system of modular parallel three-phase three-level inverter | |
| CN110212741B (en) | Power factor correction circuit, control method and controller | |
| Kakosimos et al. | A single-phase nine-level inverter for renewable energy systems employing model predictive control | |
| CN116566179A (en) | Model prediction control method for three-phase three-level T-type grid-connected inverter | |
| CN116436273A (en) | Control method and system for three-phase three-level inverter | |
| Lin et al. | NOC-based multiple low-order harmonic currents suppression method | |
| Antchev | Classical and recent aspects of active power filters for power quality improvement | |
| Hu et al. | Double‐step model predictive direct power control with delay compensation for three‐level converter | |
| Xu et al. | Model predictive duty cycle control for three‐phase Vienna rectifiers | |
| KR20200001498A (en) | Modulation method and apparatus of Modular Multi-level converter operated in nearest level control | |
| Zhou et al. | Predictive current controller and compensator-based discrete current controller for single-phase bridge inverters | |
| CN113809944B (en) | Grid-connected current tracking and circulating current prediction control method for offshore wind power flexible-direct current converter | |
| CN114785093A (en) | MMC model prediction control method of direct-drive permanent magnet wind power system | |
| CN117578899B (en) | Dynamic optimization and virtual voltage vector sliding mode prediction control method and system | |
| Sharma et al. | Neural network-based three-phase NPC rectifier for DC bus capacitor voltage balancing under perturbed mains supply conditions | |
| CN108777552B (en) | Model prediction control strategy method and system suitable for cascaded H-bridge converter | |
| CN112701951A (en) | Inverter voltage state prediction control method based on tolerant hierarchical sequence method | |
| CN110855166A (en) | PWM rectifier dead-beat current prediction control method | |
| CN111009924A (en) | Wide-range change compensation method for filter inductance value of single-phase three-level inverter |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |