Calculation-simplified multi-vector model predictive control method, device and controller

Abstract

The invention belongs to the technical field of multi-vector model predictive control, and provides a calculation-simplified multi-vector model predictive control method and system. The calculation-simplified multi-vector model prediction control method comprises the following steps: carrying out region division on a three-phase space vector diagram to form a plurality of groups of region sets, and enabling each group of region sets to be in one-to-one correspondence with one space vector; searching a plurality of neighbor space vectors for synthesizing the reference space vector according to the region set where the reference space vector is located; on the basis of a converter model, a reference space vector and all searched neighbor space vectors, all on-off state combinations of the converter are calculated on line with the purpose of minimizing the current tracking error of the converter, and no vector coordinate or state combination or other value tables need to be stored in advance; and based on all the calculated switch state combinations of the converter and the voltage control value function, selecting the switch state combination with the minimum capacitance voltage deviation to control the converter so as to realize multi-objective optimization control of the flying voltage and the midpoint voltage.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-vector model predictive control, and in particular relates to a multi-vector model predictive control method, device and controller with simplified calculation. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Renewable energy such as wind and solar energy are valued for their sustainability and cleanliness, but their inherent randomness, volatility and discontinuity pose challenges to grid stability. Especially when connected to the grid on a large scale, power quality issues such as frequency fluctuations and voltage instability may occur. Multilevel converters play an important role in the connection of renewable energy to the grid due to their advantages of improving power quality and reducing harmonic content.

[0004] Model predictive control (MPC) has fast dynamic response, simple implementation, and the ability to solve nonlinear multi-objective problems in power converters. It is widely used in power electronic converters. Among them, finite set model predictive control (FCS-MPC) optimizes in a finite discrete control set and has attracted widespread attention due to its high efficiency and applicability. In particular, it has greater flexibility by considering three-phase voltages at the same time and through redundant voltage vectors, such as capacitor voltage balance and common mode voltage elimination.

[0005] In FCS-MPC, the system model is used to predict the future output variables of each switch state, and the input that minimizes the value function is selected as the optimal vector so that it can be executed by the converter in the next sampling cycle. However, since traditional FCS-MPC needs to perform many prediction calculations in a short period of time, it brings computational challenges to the chip processing power; a lookup table is used to pre-store each voltage vector and switch state, which requires a certain amount of memory. The complex space vectors and redundancy of high-level converters will bring a great computational burden. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a multi-vector model predictive control method and system with simplified calculation, which can be applied to all hybrid multi-level converters.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A first aspect of the present invention provides a multi-vector model predictive control method with simplified calculation.

[0009] In one or more embodiments, a multi-vector model predictive control method for simplified calculation is provided, comprising:

[0010] The three-phase space vector diagram is divided into regions to form several groups of region sets, each group of region sets corresponds to a space vector one by one;

[0011] According to the region set where the reference space vector is located, searching for a number of neighboring space vectors for synthesizing the reference space vector;

[0012] Based on the converter model, the reference space vector and all the neighboring space vectors found, all the switch state combinations of the converter are calculated online with the goal of minimizing the current tracking error of the converter, without storing any vector coordinates or state combination numerical tables in advance;

[0013] Based on all the calculated switch state combinations of the converter and the voltage control cost function, the switch state combination with the minimum capacitor voltage deviation is selected to control the converter to achieve multi-objective optimization control of the flying voltage and the midpoint voltage.

[0014] As an implementation mode, a control threshold for controlling the fluctuation range of the flying voltage is preset to determine the priority of controlling the flying voltage and the midpoint voltage, and then the redundant switch state is adjusted according to the priority.

[0015] As an implementation method, the priority relationship between controlling the flying capacitor voltage and the midpoint voltage is:

[0016] The absolute value of the difference between the flying capacitor voltage and the reference value is compared with the control threshold. If the former is greater than the latter, the flying capacitor voltage balance is controlled first, otherwise the midpoint voltage is controlled first.

[0017] As an implementation method, the flying capacitor voltage and the midpoint voltage are balanced and controlled.

[0018] As an implementation method, all predicted switching state combinations of the converter are substituted into the value function of voltage control for comparison. The minimum value function corresponds to the minimum voltage deviation, and the switching state corresponding to the minimum value function is selected to control the operation of the converter's switch tube.

[0019] As an implementation manner, rounding and logical judgment are used to find several neighboring space vectors that synthesize the reference space vector.

[0020] A second aspect of the present invention provides a multi-vector model predictive control system with simplified calculations.

[0021] In one or more embodiments, a multi-vector model predictive control system with simplified calculation includes:

[0022] A space vector diagram division module, which is used to divide the three-phase space vector diagram into regions to form a plurality of region sets, each region set corresponding to a space vector one by one;

[0023] A neighboring space vector search module, which is used to search for a number of neighboring space vectors for synthesizing the reference space vector according to the area where the reference space vector is located;

[0024] A switch state combination calculation module is used to calculate all switch state combinations of the converter online based on the converter model, the reference space vector and all neighboring space vectors found, with the goal of minimizing the current tracking error of the converter, without storing any vector coordinates or state combination numerical tables in advance;

[0025] The multi-objective optimization control module is used to select the switch state combination with the minimum capacitor voltage deviation to control the converter based on all the switch state combinations and voltage control value functions of the calculated converter, so as to realize the multi-objective optimization control of the flying voltage and the midpoint voltage.

[0026] As an implementation mode, in the multi-objective optimization control module, a control threshold for controlling the fluctuation range of the flying voltage is preset to determine the priority of controlling the flying voltage and the midpoint voltage, and then the redundant switch state is adjusted according to the priority.

[0027] As an implementation mode, in the multi-objective optimization control module, the priority relationship between controlling the flying capacitor voltage and the midpoint voltage is:

[0028] The absolute value of the difference between the flying capacitor voltage and the reference value is compared with the control threshold. If the former is greater than the latter, the flying capacitor voltage balance is controlled first, otherwise the midpoint voltage is controlled first.

[0029] A third aspect of the present invention provides a controller.

[0030] A controller comprises a memory and a computer program stored in the memory and executable on the controller, wherein when the controller executes the program, the steps in the multi-vector model predictive control method with simplified calculation as described above are implemented.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] (1) The present invention first divides the three-phase space vector diagram into regions, then searches for neighboring space vectors based on the region set where the reference space vector is located, and online calculates all switch state combinations of the converter with the goal of minimizing the current tracking error of the converter. Finally, based on the voltage control value function, multi-objective optimization control of the fly-by voltage and the midpoint voltage is implemented, and online calculation of vector coordinates, redundant vectors and duty cycle is realized, avoiding the advance input of any numerical tables such as vector coordinates or state combinations, eliminating the need to store and read data, and simplifying the calculation of multi-vector model predictive control.

[0033] (2) The present invention sets the control priority of the midpoint voltage and the flying voltage, takes into account the redundant voltage vector, and selects the switch combination with the minimum capacitor voltage deviation to control the converter. This is performed in a three-phase space vector diagram. It can be extended to any multi-level converter by simply correcting the expression between the reference output voltage and the switching function.

[0034] (3) The present invention adopts an implementation method based on region division, avoiding the complex calculation process of traditional MPC. In the current tracking algorithm part, the number of calculations remains unchanged, which is conducive to the promotion and application of multi-level converters.

[0035] (4) The present invention uses simple logical judgment to find the nearest several vectors to synthesize the reference vector, so as to minimize the current tracking error. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0037] Figure 1 1 is a system structure diagram of a novel hybrid seven-level converter (T2C-HB) of a three-level T-type converter and an H-bridge cascaded according to an embodiment of the present invention;

[0038] Figure 2 is a schematic diagram of area division of a space vector diagram according to an embodiment of the present invention;

[0039] FIG. 3( a ) is a schematic diagram of a first area division according to an embodiment of the present invention.

[0040] FIG3( b ) is a schematic diagram of second region division according to an embodiment of the present invention;

[0041] Figure 4 are schematic diagrams of three minimum triangles according to an embodiment of the present invention;

[0042] Figure 5 is a third region division schematic diagram according to an embodiment of the present invention;

[0043] Figure 6 is a schematic diagram of a minimum parallelogram according to an embodiment of the present invention;

[0044] Figure 7 is a third area determination flow chart according to an embodiment of the present invention;

[0045] Figure 8 is a flying capacitor voltage and midpoint voltage control block diagram according to one embodiment of the present invention;

[0046] Fig. 9 is an overall control block diagram of a multi-vector MPC calculation method for simplified calculation according to one embodiment of the present invention;

[0047] FIG10( a ) is a three-phase output current simulation diagram according to an embodiment of the present invention;

[0048] FIG10( b ) is a flying capacitor voltage simulation diagram according to an embodiment of the present invention;

[0049] FIG10( c ) is a midpoint voltage simulation diagram according to an embodiment of the present invention;

[0050] FIG10( d ) is a simulation diagram of output line voltage according to one embodiment of the present invention;

[0051] FIG10( e ) is an output current FFT simulation diagram according to one embodiment of the present invention;

[0052] Fig.11 is a flow chart of a multi-vector model predictive control method for simplified calculation according to an embodiment of the present invention;

[0053] Fig.12 It is a schematic diagram of the structure of a multi-vector model predictive control device with simplified calculation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0056] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0057] Terminology explanation:

[0058] Model predictive control (MPC) is widely used in power electronic converters due to its fast dynamic response, simple implementation, and ability to solve nonlinear multi-objective problems. Among them, finite control set model predictive control (FCS-MPC) has attracted much attention due to its high efficiency and applicability. FCS-MPC improves control flexibility by considering redundant voltage vectors to balance capacitor voltages and reduce common-mode voltage. In the FCS-MPC method, the system model is used to predict the future output variables of each switch state and compare the value of the value function. However, traditional FCS-MPC requires multiple prediction calculations in a short time and uses a lookup table to pre-store each voltage vector and switch state, which requires a large amount of memory. At the same time, many redundant space vectors of high-level converters also cause a large computational burden, and additional constraints such as midpoint voltage balance, flying capacitor voltage balance, and common-mode voltage reduction further increase the computational requirements of FCS-MPC.

[0059] like Fig.11 As shown, a multi-vector model predictive control method with simplified calculation is provided, comprising:

[0060] S101: dividing the three-phase space vector diagram into regions to form a plurality of region sets, each region set corresponding to a space vector one by one;

[0061] S102: searching for a number of neighboring space vectors for synthesizing the reference space vector according to the region set where the reference space vector is located;

[0062] S103: Based on the converter model, the reference space vector and all the neighboring space vectors found, with the goal of minimizing the current tracking error of the converter, all the switch state combinations of the converter are calculated online without storing any vector coordinates or state combination numerical tables in advance;

[0063] S104: Based on all the calculated switch state combinations and voltage control value functions of the converter, a switch state combination with a minimum capacitor voltage deviation is selected to control the converter to achieve multi-objective optimization control of the flying voltage and the midpoint voltage.

[0064] In this embodiment, in the current tracking calculation part, the number of calculations is independent of the voltage level of the converter. For the process of finding each adjacent vector, the number of calculation operations required is fixed, the calculation cost remains unchanged, and it can be extended to any multi-level topology. Moreover, all vector coordinates and switch states of this embodiment are calculated online, without the need to store any vector coordinates or state combination numerical tables in advance, which is more conducive to extension to high-level converters. This embodiment is carried out in a three-phase space vector diagram, and it can be extended to any multi-level converter by simply correcting the expression between the reference output voltage and the switch function.

[0065] In step S102, several neighboring space vectors that synthesize the reference space vector are searched using rounding and logical judgment. This can avoid the comparison of candidate voltage vectors to reduce the computational complexity. In the process of searching and identifying each adjacent vector, the number of required computational operations is predetermined and unchanged. This means that no matter where the reference vector is located in the space vector diagram, the required computational steps and complexity remain unchanged, and the efficiency and response time of the algorithm will not change significantly with the increase in the number of levels.

[0066] In step S103, all vector coordinates and switch states are calculated in real time, which avoids the need to pre-store a large number of value tables such as vector coordinates or switch state combinations. This online calculation method significantly reduces the storage burden of the memory because it no longer needs to allocate additional memory space for various possible vector configurations.

[0067] In step S104, a control threshold for controlling the fluctuation range of the flying voltage is preset to determine the priority of controlling the flying voltage and the midpoint voltage, and then the redundant switch state is adjusted according to the priority. All the calculated switch state combinations of the converter are respectively substituted into the value function of the voltage control for comparison. The minimum value function corresponds to the minimum voltage deviation, and the switch state corresponding to the minimum value function is selected to control the operation of the switch tube of the converter.

[0068] Among them, the priority relationship between controlling the flying capacitor voltage and the midpoint voltage is:

[0069] The absolute value of the difference between the flying capacitor voltage and the reference value is compared with the control threshold. If the former is greater than the latter, the flying capacitor voltage balance is controlled first, otherwise the midpoint voltage is controlled first.

[0070] In some embodiments, the flying capacitor voltage and the midpoint voltage are balanced and controlled. In the ±E state, there are two switch states corresponding to one voltage state, respectively charging or discharging the capacitor. According to the positive and negative of the two balance functions, a suitable switch state can be selected to charge or discharge the capacitor.

[0071] There are three main topologies, midpoint clamp, flying capacitor and cascaded H-bridge, each with its own specific advantages, but also some disadvantages. To balance these disadvantages, hybrid multilevel converters have recently become an area of ​​intensive research. For example, a hybrid cascaded T+H topology uses a seven-level converter (T2C-HB) cascaded with three-level T-type and H-bridge units, which not only improves the output voltage, but also reduces the voltage requirements of the device.

[0072] Since there are two capacitors with the same capacitance in series on the DC side of the seven-level T2C-HB inverter, the voltage of the upper and lower capacitors on the DC side will be offset due to the influence of many factors during operation, causing the problem of unbalanced midpoint voltage. In addition, the three-phase flying capacitors of the seven-level T2C-HB inverter also need to be controlled simultaneously. Therefore, in order to achieve multi-objective control of midpoint voltage balance and flying capacitor voltage fluctuation suppression without introducing additional switching losses, this implementation provides a multi-vector model predictive control method that reduces flying capacitor voltage fluctuations, has midpoint balance and reduces computational complexity.

[0073] The control object of this implementation is a new hybrid seven-level converter (T2C-HB) system, such as Figure 1 As shown, each phase consists of a three-level T-type converter and an H-bridge cascaded, and the DC side includes two identical series capacitors. A midpoint O is formed between the two capacitors and connected to each phase input of the inverter. Each phase output of the inverter is connected to the grid through a filter. xn ,i x , R, L and U dc They respectively represent the voltage between points x and n, the output current, the resistance, the filter inductance, and the DC side voltage (x=a, b, c).

[0074] Assume that the DC side voltage is U dc (4E), in order for the seven-level T2C-HB inverter to work properly, the DC side capacitor voltage and the flying capacitor voltage should be controlled at U dc / 2 and U dc / 4, that is, 2E and E. When the above conditions are met, the inverter can output seven levels of -3E, -2E, -E, 0, E, 2E, and 3E, corresponding to 12 switch combinations. When the output level is E or -E, there are two redundant switch combinations, which have the effect of charging or discharging the capacitor. Therefore, the flying capacitor voltage and midpoint potential can be controlled by reasonably allocating the action time of the two redundant switch combinations.

[0075] Firstly, the discrete mathematical model of the seven-level inverter is established. According to Kirchhoff's law, u xn Can be written as

[0076]

[0077] Using the Euler approximation, (1) in the discrete model is

[0078]

[0079] where x = α, β, u * xn (k) and i * x(k+1) are the reference voltage and reference current at the kth moment and the k+1th moment respectively. x (k) is the output current at the kth moment. T s is the sampling period. The reference current i at the k+1th moment * x (k+1), the grid voltage e at the k+1th moment x (k+1) is derived as

[0080] i * x (k+1)=3i * x (k)-3i * x (k-1)+i * x (k-2) (x=a,b,c) (3)

[0081] e x (k+1)=3e x (k)-3e x (k-1)+e x (k-2) (x=α,β) (4)

[0082] like Figure 2 As shown in the figure, in the space vector diagram, for any vector, it is divided into n regions, which are called the nth region of the vector. By simply judging the region where the reference vector is located in the nth region, n adjacent vectors can be found without enumeration and iteration operations, thereby reducing the amount of calculation.

[0083] In order to simplify the calculation process, the reference vector [V α ,V β ] is normalized to [V α1 ,V β1 ]

[0084]

[0085] The relationship between the conversion between the vector in the mn coordinate system and the vector in the rectangular coordinate system αβ is:

[0086]

[0087] By rounding, we get the coordinates of the vector closest to the reference vector [m0,n0]

[0088]

[0089] In order to calculate the first nearest neighbor vector and the second nearest neighbor vector, the first area and the second area are used for judgment. FIG3(a) shows the second area of ​​O2, and FIG3(b) shows the first area of ​​O1.

[0090] Since the reference vector may be located in any area of ​​triangle I or triangle II, it can be judged by the following conditions. The judgment conditions are as follows:

[0091]

[0092] Where [V m ref ,V n ref ] is the reference vector coordinate in the mn coordinate system. Figure 4 There are three types of triangles in the figure. The vertices of each small triangle are composed of two virtual vectors with coordinates (x1, y1) and (x2, y2) and a real vector with coordinates (x3, y3). After finding the vertices of the small triangle, determine whether the vector coordinates satisfy

[0093] V m -V n =3n, n∈N (9)

[0094] A vector that meets this condition is a real vector, otherwise it is a virtual vector. The real vector relative to the first neighbor vector is the second nearest neighbor vector, with coordinates (x1+x2-x3, y1+y2-y3).

[0095] In order to calculate the third nearest neighbor vector, the third area is used to determine, such as Figure 5 As shown, the third area is divided. The relationship between the mn60° coordinate system and the rectangular coordinate system αβ established at this time is:

[0096]

[0097] After rounding, a parallelogram area is obtained. Since the reference vector may be located in any area of ​​A, B, C, or D, such as Figure 6 As shown. According to V m ref (k), V n ref (k), m0(k) and n0(k), determine [V m ref ,V n ref ] The boundary conditions of the sub-sector are as follows:

[0098]

[0099] There are four situations as follows, the judgment flow chart is as follows Figure 7 shown.

[0100]

[0101] By analogy, we can determine the nth region where the reference vector is located, and thus find the nth nearest neighbor vector. Since the coordinates and voltage vectors correspond one to one, the coordinates can be converted to obtain all redundant voltage vectors, avoiding the time-consuming table lookup method. The first nearest neighbor vector and the second nearest neighbor vector are converted using the following matrix:

[0102]

[0103] The third nearest neighbor vector is transformed using the following matrix,

[0104]

[0105] Define the base coordinates as [V a ,V b ,V c ]=[V a ,V b ,0], the redundant vector is

[0106] [V a +i,V b +i,i],i∈[n1,n2] (15)

[0107] Where n1 = max(-3-V a ,-3-V b ,-3), n2=min(3-V a ,3-V b ,3).

[0108] To calculate the vector duty cycle, taking three vectors as an example, since the three vectors and the reference vector are determined, the cost function can be rewritten as a function of distance:

[0109]

[0110] in is the reference voltage. Among them, V1, V2 and V3 are used to synthesize V ref The vector of V1, V2 and V3 is t1, t2 and t3, respectively. Assume that the value function values ​​of V1, V2 and V3 are J1, J2 and J3 respectively. The longer the distance, the shorter the action time. The duty cycle can be calculated as,

[0111]

[0112] The following equation can be derived:

[0113]

[0114] Since different redundant voltage vectors have different effects on the balance of midpoint and suspended capacitor voltages, in order to obtain the optimal switching state, the balance of capacitor voltage can be used as a constraint of the optimization problem, thereby minimizing the error between the expected capacitor voltage and the predicted voltage. Therefore, the voltage balance cost function J2 can be:

[0115]

[0116] The expression of the function BalFC that balances the flying capacitor voltage is:

[0117] BalFC=(U xf -E)i x (x=a,b,c)

[0118] The expression of the equilibrium midpoint voltage function BalNP is:

[0119] BalNP=(U N -U P )i x u xn (x=a,b,c)

[0120] Among them, U xf represents the flying capacitor voltage of phase x (x=a,b,c), U P , U N Respectively represent the voltage of the upper and lower capacitors on the DC side. In the ±E state, there are two switch states corresponding to a voltage output. The appropriate switch state is selected according to the positive or negative of BalFC or BalNP. In order to determine the priority of flying capacitor voltage and midpoint voltage control, a threshold is used for selection in the control algorithm. The flow chart is as follows: Figure 8 As shown in Figure 1, by using J2 to obtain the redundant switch state corresponding to the optimal vector, the flying capacitor voltage and the midpoint voltage can be balanced. The overall control flow diagram of the system is shown in Figure 1. Fig. 9 As shown, it includes current tracking, midpoint voltage balance control, and flying capacitor voltage control. From the above analysis, it can be seen that the proposed method is carried out in a three-phase space vector diagram and can be extended to a multi-level converter by simply modifying the relationship expression between the reference output voltage and the switching function.

[0121] The simulation results are shown in Figure 10. In Figure 10(a), the current is sinusoidal with an amplitude of 6A. In Figure 10(b), the flying capacitor voltage is 1 / 4 of the DC side, with a fluctuation of 0.5V, which is less than Figure 7 The threshold in the figure is 5V; the midpoint voltage in Figure 10(c) is half of the DC side, with an upper and lower fluctuation of 0.2V; Figure 10(d) is the line voltage; the FFT in Figure 10(e) shows that the inverter side current THD is 1.06%, and the current harmonics are effectively suppressed.

[0122] Fig.12 is a schematic diagram of a multi-vector model predictive control device for simplified calculation in an embodiment of the present invention. Fig.11 The simplified calculation of the multi-vector model predictive control method corresponds to Fig.12 As shown, the multi-vector model predictive control device with simplified calculation in this embodiment may include:

[0123] A space vector diagram division module 201, which is used to divide the three-phase space vector diagram into regions to form a plurality of region sets, each region set corresponding to a space vector one by one;

[0124] A neighboring space vector search module 202, which is used to search for a number of neighboring space vectors for synthesizing the reference space vector according to the region set where the reference space vector is located;

[0125] A switch state combination calculation module 203 is used to calculate all switch state combinations of the converter online based on the converter model, the reference space vector and all neighboring space vectors found, with the goal of minimizing the current tracking error of the converter, without storing any vector coordinates or state combination value tables in advance;

[0126] The multi-objective optimization control module 204 is used to select the switch state combination with the minimum capacitor voltage deviation to control the converter based on all the calculated switch state combinations and voltage control value function of the converter, so as to achieve multi-objective optimization control of the flying voltage and the midpoint voltage.

[0127] Specifically, in the multi-objective optimization control module 204, a control threshold for controlling the fluctuation range of the flying voltage is preset to determine the priority of controlling the flying voltage and the midpoint voltage, and then the redundant switch state is adjusted according to the priority.

[0128] In the multi-objective optimization control module 204, the priority relationship between controlling the flying capacitor voltage and the midpoint voltage is:

[0129] The absolute value of the difference between the flying capacitor voltage and the reference value is compared with the control threshold. If the former is greater than the latter, the flying capacitor voltage balance is controlled first, otherwise the midpoint voltage is controlled first.

[0130] It should be noted here that the various modules in the multi-vector model predictive control device for simplified calculation correspond one-to-one to the various steps in the multi-vector model predictive control method for simplified calculation, and their specific implementation processes are the same and will not be described in detail here.

[0131] In one or more embodiments, a controller is also provided, comprising a memory and a computer program stored in the memory and executable on the controller, wherein when the controller executes the program, the steps in the multi-vector model predictive control method with simplified calculation as described above are implemented.

[0132] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, the computer program including a computer program for executing Fig. 9 In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions defined in the device of the present application are executed.

[0133] in, Fig. 9 The computer program instructions corresponding to the method shown may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0134] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-vector model predictive control method with simplified calculation, characterized in that: include: The three-phase space vector diagram is divided into regions to form several groups of region sets, each group of region sets corresponds to a space vector one by one; According to the region set where the reference space vector is located, searching for a number of neighboring space vectors for synthesizing the reference space vector; Based on the converter model, the reference space vector and all the neighboring space vectors found, all the switch state combinations of the converter are calculated online with the goal of minimizing the current tracking error of the converter. Based on all the calculated switch state combinations of the converter and the voltage control cost function, the switch state combination with the minimum capacitor voltage deviation is selected to control the converter to achieve multi-objective optimization control of the flying voltage and the midpoint voltage.

2. The multi-vector model predictive control method for simplified calculation according to claim 1, characterized in that: A control threshold for controlling the fluctuation range of the flying voltage is preset to determine the priority of controlling the flying voltage and the midpoint voltage, and then the redundant switch state is adjusted according to the priority.

3. The multi-vector model predictive control method for simplified calculation as claimed in claim 2, characterized in that: The priority relationship between controlling the flying capacitor voltage and the midpoint voltage is: The absolute value of the difference between the flying capacitor voltage and the reference value is compared with the control threshold. If the former is greater than the latter, the flying capacitor voltage balance is controlled first, otherwise the midpoint voltage is controlled first.

4. The multi-vector model predictive control method for simplified calculation according to claim 1, characterized in that: The flying capacitor voltage and the midpoint voltage are balanced and controlled.

5. The multi-vector model predictive control method for simplified calculation according to claim 1, characterized in that: Substitute all the calculated switch state combinations of the converter into the value function of the voltage control for comparison. The minimum value function corresponds to the minimum voltage deviation. The switch state corresponding to the minimum value function is selected to control the operation of the converter's switch tube.

6. The multi-vector model predictive control method for simplified calculation according to claim 1, characterized in that: Several neighboring space vectors for synthesizing the reference space vector are found by using rounding and logical judgment.

7. A multi-vector model predictive control device with simplified calculation, characterized in that: include: A space vector diagram division module, which is used to divide the three-phase space vector diagram into regions to form a plurality of region sets, each region set corresponding to a space vector one by one; A neighboring space vector search module, which is used to search for a number of neighboring space vectors that synthesize the reference space vector according to the region set where the reference space vector is located; A switch state combination calculation module is used to calculate all switch state combinations of the converter online based on the converter model, the reference space vector and all neighboring space vectors found, with the goal of minimizing the current tracking error of the converter; A multi-objective optimization control module is used to select the switch state combination with the minimum capacitor voltage deviation to control the converter based on all the calculated switch state combinations and voltage control value functions of the converter, so as to realize multi-objective optimization control of the flying voltage and the midpoint voltage.

8. The multi-vector model predictive control device for simplified calculation according to claim 7, characterized in that: In the multi-objective optimization control module, a control threshold for controlling the fluctuation range of the flying voltage is preset to determine the priority of controlling the flying voltage and the midpoint voltage, and then the redundant switch state is adjusted according to the priority.

9. The multi-vector model predictive control device for simplified calculation according to claim 8, characterized in that: In the multi-objective optimization control module, the priority relationship between controlling the flying capacitor voltage and the midpoint voltage is: The absolute value of the difference between the flying capacitor voltage and the reference value is compared with the control threshold. If the former is greater than the latter, the flying capacitor voltage balance is controlled first, otherwise the midpoint voltage is controlled first.

10. A controller comprising a memory and a computer program stored in the memory and executable on the controller, characterized in that: When the controller executes the program, the steps in the multi-vector model predictive control method with simplified calculation as described in any one of claims 1 to 6 are implemented.

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