A Dual-Vector Unified Model Prediction Method and System Based on Sphere Decoding Theory

Abstract

The present invention relates to the technical field of new energy grid-connected power generation, and provides a dual-vector unified model prediction method and system based on the sphere decoding theory, including: within one control period, based on the two-dimensional space plane voltage vector, taking the voltage vector amplitude as the discrimination criterion, building a two-dimensional space search tree based on the sphere decoding theory; searching for reserved nodes in the first layer of the two-dimensional space search tree; searching for all nodes in the second layer under the reserved nodes in the two-dimensional space search tree to obtain the main output vector; searching for all nodes in the second layer under the reserved nodes and the reserved nodes in the two-dimensional space search tree except the main output vector to obtain the secondary output vector; after synthesizing the main output vector and the secondary output vector into the expected output value of the converter, calculating the switching state of the converter in the next control period to realize the control of the converter over the power grid. It effectively reduces the computational amount of vector search and improves the control performance over the power grid.

Description

Technical Field

[0001] The invention belongs to the technical field of new energy grid-connected power generation, and particularly relates to a dual-vector unified model prediction method and system based on sphere decoding theory. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention, and do not necessarily constitute prior art.

[0003] Renewable energy sources such as wind energy, photovoltaic arrays, and fuel cells play a crucial role in reducing the greenhouse effect and carbon dioxide emissions. As the core equipment connecting renewable energy and the power grid (or load), converters play an important role in distributed power generation systems and microgrids. Therefore, the research and development of converters are of great significance for the development of energy technology. To effectively control converters, in addition to classical linear control strategies, numerous non-linear control strategies have also been widely studied. Among them, model predictive control does not rely on a modulator and only uses a cost function to evaluate the output state. In addition, model predictive control has attracted much attention due to its advantages such as simple implementation, multi-objective optimization, and high flexibility.

[0004] Although the model predictive method has great potential, there are still some deficiencies to be improved. For example, the control accuracy of the converter using the single-vector model predictive method is low, resulting in large fluctuations in the output electrical state (voltage / current) of the converter; although the multi-vector model predictive method can improve the control accuracy, the existing multi-vector determination methods have complex evaluation processes, cumbersome vector selection, and rely on high-performance computing resources, and there are problems with poor control effects, and none of them can achieve stable control of the power grid (or load). Summary of the Invention

[0005] To solve the technical problems existing in the above background technique, the present invention provides a dual-vector unified model prediction method and system based on sphere decoding theory, which effectively reduces the computational amount of vector search through a two-dimensional space search tree based on sphere decoding theory and improves the control performance of the power grid.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] The first aspect of the present invention provides a dual-vector unified model prediction method based on sphere decoding theory, which includes:

[0008] Obtain the two-dimensional space plane voltage vector of the converter within a control period;

[0009] Based on the two-dimensional space plane voltage vector, build a two-dimensional space search tree based on sphere decoding theory with the voltage vector amplitude as the discrimination criterion;

[0010] Search for reserved nodes within the first layer of the two-dimensional space search tree;

[0011] Search for all nodes within the second layer under the reserved nodes in the two-dimensional space search tree to obtain the main output vector;

[0012] Search for all nodes except the main output vector within the reserved nodes and the second layer under the reserved nodes in the two-dimensional space search tree to obtain the secondary output vector;

[0013] After synthesizing the main output vector and the secondary output vector into the expected output value of the converter, calculate the switching state of the converter in the next control period to achieve the control of the converter over the power grid.

[0014] Further, the steps for obtaining the two-dimensional space plane voltage vector are as follows:

[0015] Obtain the switching state of the converter and the three-phase electrical state on the output side;

[0016] Based on the switching state of the converter, organize the three-phase electrical state on the output side into a three-dimensional space solid voltage vector;

[0017] Convert the three-dimensional space solid voltage vector into a two-dimensional space plane voltage vector.

[0018] Further, the method for obtaining the reserved nodes is as follows:

[0019] For all nodes within the first layer of the two-dimensional space search tree, calculate the Euclidean distance from the expected voltage output value;

[0020] The node within the first layer corresponding to the minimum Euclidean distance is the reserved node.

[0021] Further, the expected voltage output value is calculated based on the reference value of the converter.

[0022] Further, the method for obtaining the main output vector is as follows:

[0023] Among all nodes within the second layer under the reserved nodes, select the node with the minimum Euclidean distance from the expected voltage output value of the converter as the main output vector.

[0024] Further, the method for obtaining the secondary output vector is as follows:

[0025] Among all nodes within the reserved nodes and the second layer under the reserved nodes except the main output vector, select the node with the minimum Euclidean distance from the expected voltage output value of the converter as the secondary output vector.

[0026] Further, the switching state of the converter in the next control period is calculated based on the expected output value of the converter in combination with the neutral point potential state.

[0027] The second aspect of the present invention provides a dual-vector unified model prediction system based on the sphere decoding theory, which includes:

[0028] A data acquisition module configured to: acquire the two-dimensional space plane voltage vector of the converter within one control period;

[0029] A search tree construction module configured to: construct a two-dimensional space search tree based on the sphere decoding theory with the voltage vector amplitude as the discrimination criterion based on the two-dimensional space plane voltage vector;

[0030] A reserved node search module configured to: search for reserved nodes within the first layer of the two-dimensional space search tree;

[0031] A main output vector search module configured to: search for all nodes within the second layer under the reserved nodes in the two-dimensional space search tree to obtain the main output vector;

[0032] A secondary output vector search module configured to: search for all nodes except the main output vector within the reserved nodes and the second layer under the reserved nodes in the two-dimensional space search tree to obtain the secondary output vector;

[0033] A control module configured to: after synthesizing the main output vector and the secondary output vector into the expected output value of the converter, calculate the switching state of the converter in the next control period to achieve the control of the converter over the power grid.

[0034] The third aspect of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps in a dual-vector unified model prediction method based on the sphere decoding theory as described above are implemented.

[0035] The fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the steps in a dual-vector unified model prediction method based on the sphere decoding theory as described above are implemented.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] The present invention provides a dual-vector unified model predictive control method based on sphere decoding theory. To achieve the control of the power grid or load, a two-dimensional space search tree based on sphere decoding theory is proposed with the voltage vector amplitude as the division basis, optimizing the evaluation process of model prediction, which can effectively reduce the complexity of model predictive control and improve the reliability of voltage / current control.

[0038] The present invention provides a dual-vector unified model predictive control method based on sphere decoding theory. It divides the voltage vectors of the converter into two groups, corresponding to the two-layer structure of the search tree respectively, which can effectively reduce the number of node (vector) searches, reduce the computational amount of vector search, reduce the computational amount of model prediction, and improve the control performance of the power grid (or load).

[0039] The present invention provides a dual-vector unified model predictive control method based on sphere decoding theory. It always uses the same cost function within one control period, that is, the Euclidean distance with respect to the voltage expectation value. The unified cost function greatly simplifies the computational process of model prediction and reduces the dependence of the model prediction method on high-performance computing resources.

[0040] The present invention provides a dual-vector unified model predictive control method based on sphere decoding theory. Within one control period, to achieve the control of the power grid (or load), it adopts the method of synthesizing the converter expectation value with the main and auxiliary vectors (main and auxiliary nodes), which can improve the tracking accuracy of the converter for voltage / current within one control period.

[0041] The present invention provides a dual-vector unified model predictive control method based on sphere decoding theory. Within one control period, the Euclidean distance in the tree search process is reused multiple times, saving computational resources and further improving the selection speed of the main and auxiliary vectors.

[0042] The present invention provides a dual-vector unified model predictive control method based on sphere decoding theory. It realizes the optimal control of the dual vectors of the converter in the discrete domain, which is applicable to digital controllers, and this has very important significance for the application research of voltage / current control of the converter. Description of the Drawings

[0043] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention.

[0044] Figure 1 is the flowchart of the dual-vector unified model predictive control method based on sphere decoding theory in Embodiment 1 of the present invention;

[0045] Figure 2 is the schematic diagram of the two-dimensional space plane voltage vectors in Embodiment 1 of the present invention;

[0046] Figure 3 It is a schematic diagram of the two-dimensional space search tree in the first embodiment of the present invention;

[0047] Figure 4 It is a schematic diagram of the converter voltage and current control waveforms in the first embodiment of the present invention. Detailed implementation manners

[0048] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0049] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further descriptions of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0050] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless otherwise clearly specified in the context, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0051] First embodiment

[0052] This embodiment provides a dual-vector unified model prediction method based on the sphere decoding theory, as Figure 1 shown, which specifically includes the following steps:

[0053] Step 1: Within one control period (one moment), obtain the switching state of the converter, the three-phase electrical state on the output side (i.e., the voltage state of the grid or load side), and the DC side voltage state.

[0054] Among them, the converter is a three-level converter or a power router, not limited to three-level topologies such as NPC type, T type, and ANPC. The voltage level of the converter can be low voltage, medium voltage, or high voltage.

[0055] The present invention is not limited to the inverter state and is equally applicable to the rectifier state.

[0056] Step 2: Based on the switching state of the converter, organize the three-phase electrical state on the output side into a three-dimensional space solid voltage vector (which can be a voltage vector in the a-b-c coordinate system).

[0057] Step 3: After transforming the three-dimensional space solid voltage vector into a two-dimensional space plane voltage vector (candidate voltage vector) through transformation, a two-dimensional space search tree based on the sphere decoding theory is built with the voltage vector amplitude as the discrimination criterion.

[0058] The two-dimensional space plane voltage vector can be a plane vector in the α-β coordinate system or a plane vector in the d-q reference coordinate system.

[0059] Specifically, as Figure 2 shown, the two-dimensional space plane voltage vectors corresponding to the converter are divided into 6 sectors (I, II, III, IV, V, VI), including 19 candidate voltage vectors: V M1 , V M2 , V M3 , V M4 , V M5 , V M6 , V PN1 , V PN2 , V PN3 , V PN4 , V PN5 , V PN6 , V L1 , V L2 , V L3 , V L4 , V L5 , V L6 and V 0 ; In this patent, the candidate voltage vectors are divided into two groups based on the magnitude of the voltage vector. As shown in Table 1, the plane vectors in the two-dimensional space are divided into two groups of candidate voltage vectors, the first group of candidate voltage vectors and the second group of candidate voltage vectors; Based on the sphere decoding theory, the first group of candidate voltage vectors and the second group of candidate voltage vectors are built into a two-dimensional space search tree to find the double voltage vectors. As Figure 3 shown, the two-dimensional space search tree is divided into two layers. The first layer node L1 corresponds to the first group of candidate voltage vectors, and the second layer node L2 corresponds to the second group of candidate voltage vectors.

[0060] Table 1. Grouping of plane vectors in two-dimensional space

[0061] Category Candidate voltage vector First group <![CDATA[V M1 ,V M2 ,V M3 ,V M4 ,V M5 ,V M6 > Second group <![CDATA[V PN1 ,V PN2 ,V PN3 ,V PN4 ,V PN5 ,V PN6 ,V L1 ,V L2 ,V L3 ,V L4 ,V L5 ,V L6 ,V 0 >

[0062] Step 4: Search for reserved nodes within the first layer of the search tree, that is, search for reserved nodes within the first layer of the two-dimensional space search tree obtained in Step 3. Specifically, the cost function is used to calculate the Euclidean distance between the expected value of the voltage output of the converter and all nodes (all candidate voltage vectors within the first layer) in the first layer of the two-dimensional space search tree, and the reserved node (focus node) v save (k + 1) is selected based on the Euclidean distance. In other words, the method for obtaining the reserved node is: for all nodes within the first layer of the two-dimensional space search tree, calculate the Euclidean distance from the expected value of the voltage output; the node within the first layer corresponding to the minimum Euclidean distance is the reserved node.

[0063] Among them, the node corresponding to the minimum Euclidean distance within the first layer of the two-dimensional space search tree is used as the reserved node, and its discrete domain expression in the two-dimensional space plane is:

[0064]

[0065] Among them, v αβ1 (k + 1) are all the nodes within the first layer of the two-dimensional space search tree; v * αβ (k + 1) is the expected value of the voltage output of the converter; f CFsave (k + 1) represents the Euclidean distance between the nodes within the first layer of the two-dimensional space search tree and the expected value of the voltage output of the converter; H CFsave (k + 1) represents the minimum value of the Euclidean distance between the nodes within the first layer of the two-dimensional space search tree and the expected value of the voltage output of the converter.

[0066] The sector corresponding to the reserved node (focus node) where the expected main and secondary output vectors are located is used to narrow the search range.

[0067] Step 5: Search for the sub-nodes under the reserved node within the second layer of the search tree to determine the main node (main output vector), that is, search for all the nodes within the second layer under the reserved node in the two-dimensional space search tree to obtain the main output vector. Specifically, the cost function is used to calculate the Euclidean distance between the expected value of the voltage output of the converter and all the nodes within the second layer under the reserved node (all the candidate voltage vectors within the second layer under the reserved node), and the main node (main output vector) v main (k + 1) is selected based on the Euclidean distance.

[0068] Among all the nodes within the second layer under the reserved node in the two-dimensional space search tree, the node with the minimum Euclidean distance from the expected value of the voltage output of the converter is selected as the main output vector.

[0069] Among them, the Euclidean distance f CFsec (k + 1) between the nodes within the second layer under the reserved node and the expected value of the voltage output of the converter has the following expression in the two-dimensional space plane:

[0070]

[0071] Among them, v αβ2 (k + 1) are the nodes within the second layer under the reserved node.

[0072] Therefore, the expression of the main node v main (k + 1) in the two-dimensional space plane is:

[0073] v main (k + 1) = arg[H CFmain(k + 1)]

[0074] = arg{min[H CFsave (k + 1), f CFsec (k + 1)]}

[0075] wherein, H CFmain (k + 1) represents the minimum value of the Euclidean distance between the expected voltage output and the nodes within the second layer under the reserved nodes.

[0076] Step 6: Determine the secondary node (secondary output vector), that is, search for the reserved node and all nodes other than the main node within the second layer under the reserved node in the two-dimensional space search tree to obtain the secondary output vector. Among the reserved node obtained in Step 4 and all nodes other than the main node obtained in Step 5 within the second layer under the reserved node, select the node with the minimum Euclidean distance from the expected voltage output of the converter as the secondary node (secondary output vector) v sub (k + 1), and its expression in the two-dimensional space plane is:

[0077] v sub (k + 1) = arg[H CFsub (k + 1)]

[0078] = arg[min H sec (k + 1)]

[0079] wherein, take the reserved node and all nodes other than the main node within the second layer under the reserved node as candidate secondary nodes, and H CFsub (k + 1) represents the minimum value of the Euclidean distance between the expected voltage output and the candidate secondary nodes, and H sec (k + 1) is the set of Euclidean distances between the candidate secondary nodes and the expected voltage output, that is

[0080] In Steps 4, 5, and 6, the Euclidean distance is calculated through the unified cost function f CF (k + 1):

[0081]

[0082] wherein, v αβ (k + 1) is the candidate voltage vector (two-dimensional space plane voltage vector) of the converter, v * αβ (k + 1) is the expected voltage output of the converter, and the expected voltage output of the converter is calculated based on the reference value of the converter. Specifically, the expected voltage output v * αβ (k + 1) at time k + 1 is based on the reference value x of the converter at time k + 1* αβ The state values x of the converter at (k + 1) and k moments αβ are calculated at (k), and the calculation can refer to the patent "Model Predictive Voltage Control Method for Power Inverter Circuit of Electric Energy Router" (Application No.: 2022105492628).

[0083] The Euclidean distance with respect to the voltage expectation value is mainly applied to the model predictive evaluation of the converter, that is, the main output vector and the secondary output vector are determined by using a two-dimensional space search tree.

[0084] Step 7: Synthesize the expected output value of the main and secondary nodes. Synthesize the main output vector and the secondary output vector into the expected output value of the converter, and optimize and adjust the switching state of the converter in combination with the DC-side voltage state to obtain the switching state of the converter in the next control period, realize the control of the converter, and further realize the control of the converter over the power grid (or load), that is, realize the control of the voltage, current or power of the power grid (or load).

[0085] The switching state of the converter is optimized and adjusted according to factors such as the midpoint potential state, etc., so as to obtain the optimal output voltage of the converter, and finally realize the optimal tracking of the reference value (voltage or current) of the converter.

[0086] Such as Figure 4 shown, is a schematic diagram of the converter output voltage (v a , v b , v c ) and current waveforms (i oa , i ob , i oc ). It can be seen from the simulation results that the converter adopting the control method of the present invention can realize voltage and current control, has good performance, and can effectively improve the performance of the converter.

[0087] In this embodiment, to realize the control of the power grid (or load), based on the division of the voltage vector amplitude, a two-dimensional space search tree based on the sphere decoding theory is proposed to optimize the evaluation process of the converter control, which can effectively reduce the complexity of the converter control and improve the reliability of the voltage / current control.

[0088] In this embodiment, the voltage vectors of the converter are divided into two groups, corresponding to the two-layer structure of the search tree respectively, which can effectively reduce the number of node (vector) searches, reduce the computational amount of vector search, reduce the computational amount of the converter control, and improve the control performance of the power grid (or load).

[0089] In this embodiment, the same cost function is always used within one control period, that is, the Euclidean distance with respect to the voltage expectation value. The unified cost function greatly simplifies the calculation process of the converter control and reduces the dependence of the converter control method on high-performance computing resources.

[0090] In one control period of this embodiment, to achieve the control of the power grid (or load), a method of synthesizing the expected values of the main and auxiliary vectors (main and auxiliary nodes) of the converter is adopted, which can improve the tracking accuracy of the converter for voltage / current within one control period.

[0091] In one control period of this embodiment, the Euclidean distance in the tree search process is reused multiple times, saving computing resources and further improving the selection speed of the main and auxiliary vectors.

[0092] In the discrete domain, this embodiment realizes the optimal control of the dual vectors of the converter, which is applicable to digital controllers and is of great significance for the application research of voltage / current control of the converter.

[0093] Embodiment 2

[0094] This embodiment provides a dual-vector unified model predictive system based on the sphere decoding theory, which specifically includes the following modules:

[0095] Data acquisition module, which is configured to: acquire the two-dimensional space plane voltage vector of the converter within one control period;

[0096] Search tree construction module, which is configured to: based on the two-dimensional space plane voltage vector, construct a two-dimensional space search tree based on the sphere decoding theory with the voltage vector amplitude as the discrimination criterion;

[0097] Reserved node search module, which is configured to: search for reserved nodes within the first layer of the two-dimensional space search tree;

[0098] Main output vector search module, which is configured to: search for all nodes within the second layer under the reserved nodes in the two-dimensional space search tree to obtain the main output vector;

[0099] Auxiliary output vector search module, which is configured to: search for all nodes except the main output vector within the reserved nodes and the second layer under the reserved nodes in the two-dimensional space search tree to obtain the auxiliary output vector;

[0100] Control module, which is configured to: after synthesizing the main output vector and the auxiliary output vector into the expected output value of the converter, calculate the switching state of the converter in the next control period to achieve the control of the converter over the power grid.

[0101] It should be noted here that each module in this embodiment corresponds to each step in Embodiment 1 one by one, and their specific implementation processes are the same, so they will not be repeated here.

[0102] Embodiment 3

[0103] This embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps in a dual-vector unified model prediction method based on sphere decoding theory as described in the first embodiment above.

[0104] Embodiment 4

[0105] This embodiment provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps in a dual-vector unified model prediction method based on sphere decoding theory as described in the first embodiment above.

[0106] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories and optical memories, etc.) containing computer-usable program code.

[0107] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0108] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including instruction means, and the instruction means implements the functions specified in one Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide means for implementing the functions specified in one Figure 1One process or multiple processes and / or boxes Figure 1 Steps of functions specified in one box or multiple boxes.

[0110] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0111] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A dual-vector unified model predictive method based on sphere decoding theory, characterized in that, it includes: Within a control period, obtain the two-dimensional space plane voltage vector of the converter; Based on the two-dimensional space plane voltage vector, taking the voltage vector amplitude as the discrimination criterion, build a two-dimensional space search tree based on sphere decoding theory; Search for reserved nodes within the first layer of the two-dimensional space search tree; Search for all nodes within the second layer under the reserved nodes in the two-dimensional space search tree to obtain the main output vector; Search for all nodes within the second layer under the reserved nodes and except the main output vector in the two-dimensional space search tree to obtain the secondary output vector; After synthesizing the main output vector and the secondary output vector into the expected output value of the converter, calculate the switching state of the converter in the next control period to achieve the control of the converter over the power grid; The method for obtaining the reserved nodes is: for all nodes within the first layer of the two-dimensional space search tree, calculate the Euclidean distance from the expected voltage output value; the node within the first layer corresponding to the minimum Euclidean distance is the reserved node; The discrete domain expression of the reserved node in the two-dimensional space plane is: Among them, v αβ1 (k + 1) are all the nodes within the first layer of the two-dimensional space search tree; v * αβ (k + 1) is the expected value of the voltage output of the converter; f CFsave (k + 1) represents the Euclidean distance between the nodes within the first layer of the two-dimensional space search tree and the expected value of the voltage output of the converter; H CFsave (k + 1) represents the minimum value of the Euclidean distance between the nodes within the first layer of the two-dimensional space search tree and the expected value of the voltage output of the converter; The method for obtaining the main output vector is: among all nodes within the second layer under the reserved node, select the node with the minimum Euclidean distance from the expected voltage output value of the converter as the main output vector; The main output vector v main (k + 1) has the following expression in the two-dimensional space plane: v main (k + 1) = arg[H CFmain (k + 1)] = arg{min[H CFsave (k + 1), f CFsec (k + 1)]} Among them, H CFmain (k + 1) represents the minimum value of the Euclidean distance between the expected value of the voltage output and the nodes in the second layer under the reserved nodes; The method for obtaining the secondary output vector is: among all nodes within the second layer under the reserved node and except the main output vector, select the node with the minimum Euclidean distance from the expected voltage output value of the converter as the secondary output vector; The secondary output vector v sub (k + 1) in the two-dimensional space plane is expressed as: v sub (k + 1) = arg[H CFsub (k + 1)] = arg[min H sec (k + 1)] Among them, the reserved node and all nodes except the main node in the second layer under the reserved node are used as candidate secondary nodes, H CFsub (k + 1) represents the minimum value of the Euclidean distance between the expected voltage output and the candidate secondary nodes, H sec (k + 1) is the set of Euclidean distances between the candidate secondary nodes and the expected voltage output, that is 2. A dual-vector unified model predictive method based on sphere decoding theory as described in claim 1, characterized in that, The steps for obtaining the two-dimensional space plane voltage vector are: Obtain the switching state of the converter and the three-phase electrical state on the output side; Based on the switching state of the converter, organize the three-phase electrical state on the output side into a three-dimensional space solid voltage vector; Convert the three-dimensional space solid voltage vector into a two-dimensional space plane voltage vector.

3. A dual-vector unified model predictive method based on sphere decoding theory as described in claim 1, characterized in that, The expected voltage output value is calculated based on the reference value of the converter.

4. A dual-vector unified model predictive method based on sphere decoding theory as described in claim 1, characterized in that, The switching state of the converter in the next control period is calculated based on the expected output value of the converter in combination with the midpoint potential state.

5. A dual-vector unified model predictive system based on sphere decoding theory, characterized in that, it includes: A data acquisition module configured to: within a control period, obtain the two-dimensional space plane voltage vector of the converter; A search tree building module configured to: based on the two-dimensional space plane voltage vector, taking the voltage vector amplitude as the discrimination criterion, build a two-dimensional space search tree based on sphere decoding theory; A reserved node search module configured to: search for reserved nodes within the first layer of the two-dimensional space search tree; The main output vector search module is configured to: search all nodes in the second layer under the reserved node in the two-dimensional space search tree to obtain the main output vector; The secondary output vector search module is configured to: search all nodes except the main output vector in the reserved node and the second layer under the reserved node in the two-dimensional space search tree to obtain the secondary output vector; The control module is configured to: after synthesizing the main output vector and the secondary output vector into the expected output value of the converter, calculate the switching state of the converter in the next control period to achieve the control of the converter over the power grid; The method for obtaining the reserved node is: for all nodes in the first layer of the two-dimensional space search tree, calculate the Euclidean distance from the expected value of the voltage output; the node in the first layer corresponding to the minimum Euclidean distance is the reserved node; The discrete domain expression of the reserved node in the two-dimensional space plane is: where, v αβ1 (k + 1) are all the nodes within the first layer of the two-dimensional space search tree; v * αβ (k + 1) is the expected value of the voltage output of the converter; f CFsave (k + 1) represents the Euclidean distance between the nodes within the first layer of the two-dimensional space search tree and the expected value of the voltage output of the converter; H CFsave (k + 1) represents the minimum value of the Euclidean distance between the nodes within the first layer of the two-dimensional space search tree and the expected value of the voltage output of the converter; The method for obtaining the main output vector is: among all nodes in the second layer under the reserved node, select the node with the minimum Euclidean distance from the expected value of the voltage output of the converter as the main output vector; The main output vector v main (k + 1) in the two-dimensional space plane is expressed as: v main (k + 1) = arg[H CFmain (k + 1)] = arg{min[H CFsave (k + 1), f CFsec (k + 1)]} Among them, H CFmain (k + 1) represents the minimum value of the Euclidean distance between the expected value of the voltage output and the nodes within the second layer under the reserved nodes; The method for obtaining the secondary output vector is: among all nodes in the reserved node and the second layer under the reserved node except the main output vector, select the node with the minimum Euclidean distance from the expected value of the voltage output of the converter as the secondary output vector; The secondary output vector v sub (k + 1) has the following expression in a two-dimensional space plane: v sub (k + 1) = arg[H CFsub (k + 1)] = arg[min H sec (k + 1)] Among them, the reserved node and all nodes except the main node in the second layer under the reserved node are used as candidate secondary nodes, H CFsub (k + 1) represents the minimum value of the Euclidean distance between the expected voltage output and the candidate secondary nodes, H sec (k + 1) is the set of Euclidean distances between the candidate secondary nodes and the expected voltage output, that is 6. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the program is executed by a processor, it implements the steps in a dual-vector unified model prediction method based on the sphere decoding theory as described in any one of claims 1-4.

7. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, when the processor executes the program, it implements the steps in a dual-vector unified model prediction method based on the sphere decoding theory as described in any one of claims 1-4.

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