Modular multilevel converter finite control set model predictive control method

Abstract

The application discloses a modular multilevel converter limited control set model predictive control method, obtains a cost function, a modular multilevel converter prediction model, a first sub-module set, a first sub-module voltage set and a first current set; based on the first sub-module set and the first sub-module voltage set, a voltage sorting algorithm and an optimal sub-module selection algorithm are used for processing to obtain a second sub-module voltage set; based on the second sub-module voltage set and the first current set, the modular multilevel converter prediction model is used for processing to obtain a target prediction value set; based on the target prediction value set, the cost function is used for control to obtain a limited control set model predictive control result of the modular multilevel converter. Through the optimal sub-module selection algorithm, the number of sub-modules available for selection in the modular multilevel converter is reduced, the calculation amount of the limited control set model predictive control is reduced, and meanwhile, the output performance of the modular multilevel converter is ensured.

Description

Technical Field

[0001] This invention relates to the field of high voltage direct current transmission control technology, specifically to a modular multilevel converter finite control set model predictive control method. Background Technology

[0002] High-voltage direct current (HVDC) transmission projects are mainly used for long-distance power transmission and serve as key transmission channels for large-scale wind, solar, and energy storage bases. Modular multilevel converters (MMCs) have become the mainstream topology for HVDC transmission due to their advantages such as good output performance, low harmonic content, low losses, low switching frequency, and ease of expansion. Therefore, the operation and control of MMCs is an extremely important research topic in the field of HVDC control. In recent years, given their multi-objective processing capabilities, fast dynamic response, ability to handle nonlinear systems, direct and easy implementation, and flexible constraint addition capabilities, researchers have introduced model predictive control into the operation and control of MMCs, and have conducted extensive research on this topic.

[0003] Model predictive control is mainly divided into two categories: continuous-state model predictive control (CCS-MPC) and discrete-state finite-set model predictive control (FCS-MPC). Because FCS-MPC has advantages over CCS-MPC, such as lower computational complexity, no need for a modulator, and no need to comprehensively consider the parameter design of various time-domain objective functions, it has become a focus of research on model predictive control for electronic converters.

[0004] However, FCS-MPC also faces some technical challenges. When applied to high-voltage, high-power MMC systems, the large number of sub-modules in these systems places a significant computational burden on FCS-MPC, which in turn affects the performance of the control system. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a model predictive control method involving a modular multilevel converter finite control set, in order to solve the technical problem in the prior art that when model predictive control is applied to a high-voltage, high-power MMC system, the computational burden is large, which in turn affects the performance of the control system.

[0006] The technical solution proposed in this invention is as follows:

[0007] In a first aspect, embodiments of the present invention provide a finite control set model predictive control method for a modular multilevel converter. This method includes: obtaining a cost function, a prediction model for the modular multilevel converter, a first sub-module set of the modular multilevel converter within a first control cycle, and a first sub-module voltage set and a first current set of the modular multilevel converter within a second control cycle, wherein the second control cycle is the next control cycle adjacent to the first control cycle; based on the first sub-module set and the first sub-module voltage set, processing them using a voltage sorting algorithm and an optimal sub-module selection algorithm to obtain a second sub-module voltage set of the modular multilevel converter within the second control cycle; based on the second sub-module voltage set and the first current set, processing them using the prediction model for the modular multilevel converter to obtain a target predicted value set; and based on the target predicted value set, obtaining the finite control set model predictive control result of the modular multilevel converter through cost function control.

[0008] In conjunction with the first aspect, in one possible implementation of the first aspect, the first submodule set includes the number of first bridge leg conducting submodules, the number of first upper bridge arm conducting submodules, and the number of first lower bridge arm conducting submodules corresponding to the modular multilevel converter within the first control cycle; the first submodule voltage set includes the voltage of each upper bridge arm submodule and the voltage of each lower bridge arm submodule in the modular multilevel converter within the first control cycle; the first current set includes the rectifier-side AC current value and the rectifier-side circulating current value of the modular multilevel converter within the second control cycle.

[0009] In conjunction with the first aspect, in another possible implementation of the first aspect, based on the first submodule set and the first submodule voltage set, after processing by a voltage sorting algorithm and an optimal submodule selection algorithm, the second submodule voltage set of the modular multilevel converter within the second control cycle is obtained, including: based on the number of first bridge leg conducting submodules, the number of first upper bridge arm conducting submodules, and the number of first lower bridge arm conducting submodules, after processing by the optimal submodule selection algorithm, the number of second upper bridge arm conducting submodules of the modular multilevel converter within the second control cycle and... The number of second lower arm conducting submodules; based on the voltage of each upper arm submodule and each lower arm submodule, the voltage sorting algorithm is used to obtain the voltage matrix of the upper arm submodule and the voltage matrix of the lower arm submodule; based on the number of second upper arm conducting submodules, the number of second lower arm conducting submodules, the voltage matrix of the upper arm submodules, and the voltage matrix of the lower arm submodules, the voltage set of the upper arm conducting submodules and the voltage set of the lower arm conducting submodules are determined; based on the voltage set of the upper arm conducting submodules and the voltage set of the lower arm conducting submodules, the voltage set of the second submodule is determined.

[0010] In conjunction with the first aspect, in another possible implementation of the first aspect, determining the voltage set of the upper arm conducting submodules and the voltage set of the lower arm conducting submodules based on the number of the second upper arm conducting submodules, the number of the second lower arm conducting submodules, the voltage matrix of the upper arm submodules, and the voltage matrix of the lower arm submodules includes: determining the voltage set of the upper arm conducting submodules in the voltage matrix of the upper arm submodules based on the number of the second upper arm conducting submodules; and determining the voltage set of the lower arm conducting submodules in the voltage matrix of the lower arm submodules based on the number of the second lower arm conducting submodules.

[0011] In conjunction with the first aspect, in another possible implementation of the first aspect, obtaining the cost function includes: obtaining a second current set of the modular multilevel converter during the second control cycle, the second current set reflecting the current reference value of the modular multilevel converter; and establishing the cost function based on the second current set and the target predicted value set.

[0012] In conjunction with the first aspect, in another possible implementation of the first aspect, based on the target predicted value set and controlled by the cost function, the finite control set model predictive control result of the modular multilevel converter is obtained, including: determining a target on-line submodule based on the target predicted value set and the cost function; when the target on-line submodule is determined, the modular multilevel converter sends an on-line control signal to the target on-line submodule and sends a off-line signal to other on-line submodules in the modular multilevel converter; based on the on-line control signal and the off-line signal, the modular multilevel converter is controlled within the second control cycle, and the finite control set model predictive control result of the modular multilevel converter is obtained.

[0013] Secondly, embodiments of the present invention provide a modular multilevel converter finite control set model predictive control device, which includes: an acquisition module for acquiring a cost function, a modular multilevel converter prediction model, a first sub-module set of the modular multilevel converter in a first control cycle, and a first sub-module voltage set and a first current set of the modular multilevel converter in a second control cycle, wherein the second control cycle is the next control cycle adjacent to the first control cycle; a first processing module for processing the first sub-module set and the first sub-module voltage set using a voltage sorting algorithm and an optimal sub-module selection algorithm to obtain a second sub-module voltage set of the modular multilevel converter in the second control cycle; a second processing module for processing the second sub-module voltage set and the first current set using the modular multilevel converter prediction model to obtain a target predicted value set; and a control module for controlling the target predicted value set using the cost function to obtain the finite control set model predictive control result of the modular multilevel converter.

[0014] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing a computer program for causing the computer to execute the modular multilevel converter finite control set model predictive control method as described in the first aspect and any one of the embodiments of the present invention.

[0015] Fourthly, embodiments of the present invention provide an electronic device, including: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores a computer program, and the processor executes the computer program to perform the modular multilevel converter finite control set model predictive control method as described in the first aspect and any one of the embodiments of the present invention.

[0016] The technical solution provided by this invention has the following effects:

[0017] The modular multilevel converter finite control set model predictive control method provided in this embodiment of the invention reduces the number of selectable sub-modules in the modular multilevel converter and the computational load of finite control set model predictive control by using an optimal sub-module selection algorithm, while ensuring the output performance of the modular multilevel converter. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a modular multilevel converter finite control set model predictive control method provided by an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of the k-th control cycle situation and the corresponding k+1-th control cycle selectable sub-module conduction status provided by an embodiment of the present invention;

[0021] Figure 3 This is a detailed implementation diagram of a model predictive control method for optimal input submodules in MMC provided by an embodiment of the present invention;

[0022] Figure 4 This is a structural block diagram of a modular multilevel converter finite control set model predictive control device provided in an embodiment of the present invention;

[0023] Figure 5 This is a schematic diagram of the structure of a computer-readable storage medium provided according to an embodiment of the present invention;

[0024] Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] This invention provides a modular multilevel converter finite control set model predictive control method, such as... Figure 1 As shown, the method includes the following steps:

[0028] Step 101: Obtain the cost function, the modular multilevel converter prediction model, the first sub-module set of the modular multilevel converter in the first control cycle, and the first sub-module voltage set and first current set of the modular multilevel converter in the second control cycle.

[0029] The second control cycle is the next control cycle adjacent to the first control cycle. In this embodiment of the invention, the first control cycle is the k-th control cycle and the second control cycle is the (k+1)-th control cycle, which is used as an example for explanation.

[0030] Cost function tables are used to constrain the control process;

[0031] The Modular Multilevel Converter (MMC) prediction model can be used to predict the AC current, circulating current, and arm energy of a MMC.

[0032] Specifically, based on Kirchhoff's current and voltage laws and the backward Euler method, the predicted values ​​of AC current, circulating current, and bridge arm energy can be obtained, as shown in equations (1), (2), and (3) below:

[0033]

[0034] In the formula: i sj (k+1) represents the predicted AC current of the modular multilevel converter during the (k+1)th control cycle; T s Indicates the control cycle; L a Indicates bridge arm inductance; R a Indicates the possible bridge arm resistance; U represents the reference value of the DC bus voltage during the (k+1)th control cycle; uj (k+1) represents the upper arm voltage value in the modular multilevel converter during the (k+1)th control cycle; U lj (k+1) represents the lower arm voltage value in the modular multilevel converter during the (k+1)th control cycle; i sj (k) represents the sampled AC current value on the rectifier side of the modular multilevel converter;

[0035]

[0036] In the formula: i cj (k+1) represents the predicted circulating current of the modular multilevel converter in the (k+1)th control cycle; This represents the reference value of the AC side voltage of phase j during the (k+1)th control cycle; i cj (k) represents the rectifier-side circulating current value of the modular multilevel converter obtained by sampling;

[0037]

[0038] In the formula: W ij (k+1) represents the predicted value of the bridge arm energy of the modular multilevel converter in the (k+1)th control cycle; C represents the capacitance value of the sub-module in the modular multilevel converter; N represents the number of bridge arm sub-modules in the modular multilevel converter. This represents the sum of the voltages of the upper or lower bridge arm connected to the submodule during the (k+1)th control cycle.

[0039] Furthermore, the first submodule set reflects the number of different conducting submodules in the modular multilevel converter within the first control cycle; the first submodule voltage set reflects the voltage values ​​of different submodules in the modular multilevel converter within the second control cycle; and the first current set reflects the different current values ​​of the modular multilevel converter within the second control cycle.

[0040] Step 102: Based on the first submodule set and the first submodule voltage set, the second submodule voltage set of the modular multilevel converter is obtained by processing with the voltage sorting algorithm and the optimal submodule selection algorithm during the second control cycle.

[0041] The voltage sorting algorithm follows the following sorting principle: when the current is positive, it is in ascending order; when the current is negative, it is in descending order.

[0042] The principle followed by the optimal submodule selection algorithm is: a total of N or N+1 submodules can be turned on in the bridge leg of the modular multilevel converter (the bridge leg contains a total of 2N submodules).

[0043] Specifically, by processing the first submodule set using the optimal submodule selection algorithm, the submodule set within the k+1 control cycle can be obtained; by processing the voltage set of the first submodule using the voltage sorting algorithm, the voltage values ​​corresponding to different submodules within the k+1 control cycle after the optimal submodule selection algorithm can be obtained.

[0044] Furthermore, based on the module set within the k+1 control cycle and the voltage values ​​corresponding to different sub-modules after the optimal sub-module selection algorithm within the k+1 control cycle, the second sub-module voltage set of the modular multilevel converter within the k+1 control cycle can be obtained.

[0045] Step 103: Based on the voltage set of the second submodule and the first current set, the target predicted value set is obtained through the modular multilevel converter prediction model.

[0046] Specifically, based on the modular multilevel converter prediction model established in step 101, the obtained second sub-module voltage set and the sampled first current set are input into the modular multilevel converter prediction model to obtain the target prediction value set, including the AC current prediction value, circulating current prediction value and arm energy prediction value of the modular multilevel converter.

[0047] Step 104: Based on the target predicted value set, and through the cost function control, obtain the finite control set model predictive control result of the modular multilevel converter.

[0048] Specifically, the predicted values ​​in the target predicted value set are substituted into the cost function obtained in step 101. When the cost function is minimized, the finite control set model predictive control result of the modular multilevel converter can be obtained.

[0049] The modular multilevel converter finite control set model predictive control method provided in this embodiment of the invention reduces the number of selectable sub-modules in the modular multilevel converter and the computational load of finite control set model predictive control by using an optimal sub-module selection algorithm, while ensuring the output performance of the modular multilevel converter.

[0050] As an optional implementation of this invention, according to the description in step 101, the first submodule set includes the number of first bridge leg conducting submodules, the number of first upper bridge arm conducting submodules, and the number of first lower bridge arm conducting submodules corresponding to the modular multilevel converter in the first control cycle; the first submodule voltage set includes the voltage of each upper bridge arm submodule and the voltage of each lower bridge arm submodule in the modular multilevel converter in the first control cycle; the first current set includes the rectifier-side AC current value and the rectifier-side circulating current value of the modular multilevel converter in the second control cycle.

[0051] As an optional implementation of this invention, based on the first sub-module set and the first sub-module voltage set, a second sub-module voltage set of the modular multilevel converter within the second control cycle is obtained through voltage sorting algorithm and optimal sub-module selection algorithm, including:

[0052] Based on the number of the first bridge leg conducting submodules, the number of the first upper bridge arm conducting submodules, and the number of the first lower bridge arm conducting submodules, the optimal submodule selection algorithm is used to obtain the number of the second upper bridge arm conducting submodules and the number of the second lower bridge arm conducting submodules of the modular multilevel converter within the second control cycle. Based on the voltage of each upper bridge arm submodule and the voltage of each lower bridge arm submodule, the voltage sorting algorithm is used to obtain the voltage matrix of the upper bridge arm submodules and the voltage matrix of the lower bridge arm submodules. Based on the number of the second upper bridge arm conducting submodules, the number of the second lower bridge arm conducting submodules, the voltage matrix of the upper bridge arm submodules, and the voltage matrix of the lower bridge arm submodules, the voltage set of the upper bridge arm conducting submodules and the voltage set of the lower bridge arm conducting submodules are determined. Based on the voltage set of the upper bridge arm conducting submodules and the voltage set of the lower bridge arm conducting submodules, the voltage set of the second submodules is determined.

[0053] First, based on the principle followed by the optimal submodule selection algorithm in step 102, the number of selectable submodules for the bridge leg, upper arm, and lower arm in the k+1 control cycle can be obtained from the number of submodules that are conducting in the MMC bridge leg, the upper arm, and the lower arm in the k control cycle.

[0054] Therefore, the situation in the k-th control cycle and the corresponding submodule conduction situations available in the (k+1)-th control cycle are as follows: Figure 2 As shown, regardless of the conduction status in the k-th control cycle, there are three possible scenarios to choose from in the k+1-th control cycle.

[0055] Secondly, the voltage sorting algorithm is used to sort the voltages of each upper arm submodule and each lower arm submodule in the sampled first submodule voltage set to form upper and lower arm submodule voltage matrices.

[0056] Then, based on the number of the second upper arm conducting submodules, the number of the second lower arm conducting submodules, the voltage matrix of the upper arm submodules, and the voltage matrix of the lower arm submodules, the voltage sets of the upper arm conducting submodules and the voltage sets of the lower arm conducting submodules are determined, including: determining the voltage set of the upper arm conducting submodules in the voltage matrix of the upper arm submodules based on the number of the second upper arm conducting submodules; and determining the voltage set of the lower arm conducting submodules in the voltage matrix of the lower arm conducting submodules based on the number of the second lower arm conducting submodules.

[0057] Specifically, based on the optimal submodule selection algorithm, the number S of submodules S in which the upper and lower bridge arms are conducting in the three cases of bridge arm conduction in the (k+1)th control cycle is determined. ju (k+1),S jl (k+1), the voltage matrix of the upper and lower bridge arm submodules before S ju (k+1),S jl The sum of (k+1) voltage values ​​yields the voltage U of the upper and lower bridge arm conduction submodules corresponding to the three cases in the (k+1)th control cycle. uj (k+1), U lj (k+1), that is, the upper arm conduction submodule voltage set includes the upper arm conduction submodule voltage U corresponding to the three cases in the (k+1)th control cycle. uj (k+1); The voltage set of the lower arm conduction submodule includes the lower arm conduction submodule voltage U corresponding to the three cases in the (k+1)th control cycle. lj (k+1).

[0058] Finally, based on the voltage sets of the upper arm conducting submodule and the lower arm conducting submodule obtained above, the corresponding second submodule voltage set can be obtained.

[0059] Furthermore, the rectifier-side AC current value i from the first current set obtained by sampling... sj (k) Rectifier-side circulating current value i cj (k) and the corresponding upper and lower bridge arm conduction submodule voltages U in each case during the (k+1)th control cycle obtained above. uj (k+1), U lj Substituting (k+1) into the above relationships (1), (2), and (3) respectively, we can calculate the predicted values ​​of AC side current, circulating current, and upper and lower arm energy for each case.

[0060] As an optional implementation of this invention, obtaining the cost function includes: obtaining the second current set of the modular multilevel converter during the second control cycle; and establishing the cost function based on the second current set and the target predicted value set.

[0061] The second current set reflects the current reference value of the modular multilevel converter, and may include the AC side current reference value and the circulating current reference value.

[0062] Specifically, a cost function is established based on the second current set and the target predicted value set, as shown in the following equation (4):

[0063]

[0064] In the formula: λ1, λ2, and λ3 all represent the coefficients of the cost function; This represents the reference value of the AC current on phase j during the (k+1)th control cycle. This represents the reference value of the circulating current on the j-phase side during the (k+1)th control cycle; W uj (k+1) represents the predicted energy value of the upper arm, which can be obtained from the above relationship (3); W lj (k+1) represents the predicted energy value of the lower arm, which can be obtained from the above relationship (3).

[0065] As an optional implementation of this invention, based on the target predicted value set and controlled by the cost function, the finite control set model predictive control result of the modular multilevel converter is obtained, including: determining a target on-line submodule based on the target predicted value set and the cost function; when the target on-line submodule is determined, the modular multilevel converter sends an on-line control signal to the target on-line submodule and sends a off-line signal to other on-line submodules in the modular multilevel converter; based on the on-line control signal and the off-line signal, the modular multilevel converter is controlled within the second control cycle, and the finite control set model predictive control result of the modular multilevel converter is obtained.

[0066] Specifically, the predicted values ​​in the target predicted value set are substituted into the cost function shown in the above relation (4). When the cost function J is minimized, the modular multilevel converter sends a turn-on control signal to the corresponding turn-on sub-module (i.e. the target turn-on sub-module) in this case, and sends a turn-off signal to the other turn-on sub-modules. Thus, the control of one cycle is completed, and the corresponding finite control set model predictive control result is obtained.

[0067] In one example, an optimal input submodule model predictive control method (OIS-MPC) is provided, and the specific technical solution is as follows: Figure 3 As shown.

[0068] Among them, U u (k+1), U l (k+1) represent the upper and lower arm voltage values ​​of the modular multilevel converter during the (k+1)th control cycle; S nijI represents the predictive control results of a finite control set model for a modular multilevel converter; lj (k), I uj (k) represent the upper and lower arm current values ​​of the modular multilevel converter during the k-th control cycle; U nij (k) The voltage value of each submodule (including upper and lower bridge arm submodules) in the modular multilevel converter during the k-th control cycle; N uj (k), N lj (k) represents the number of upper and lower bridge arm conducting submodules in the modular multilevel converter during the k-th control cycle; M ij (k+1) represents the number of sub-modules (including upper and lower bridge arm sub-modules) in the modular multilevel converter that are turned on during the k+1th control cycle.

[0069] Furthermore, by comparing the number of input submodules N, their corresponding boundary values, and the corresponding computational load of the MMC optimal input submodule model predictive control method with those of the traditional FCS-MPC control method, it can be found that the MMC optimal input submodule model predictive control method provided in this embodiment of the invention reduces the computational load by 37.5% to 50% compared with Modified-IL-MPC, which has a smaller computational load in FCS-MPC, while ensuring good output performance.

[0070] The comparison results are shown in Table 1 below:

[0071] Table 1. Number of Submodules N and their corresponding boundary values ​​and computational complexity

[0072]

[0073] This invention also provides a modular multilevel converter finite control set model predictive control device, such as... Figure 4 As shown, the device includes:

[0074] The acquisition module 401 is used to acquire the cost function, the modular multilevel converter prediction model, the first sub-module set of the modular multilevel converter in the first control cycle, and the first sub-module voltage set and first current set of the modular multilevel converter in the second control cycle. The second control cycle is the next control cycle adjacent to the first control cycle. For details, please refer to the relevant description of step 101 in the above method embodiment.

[0075] The first processing module 402 is used to obtain the second sub-module voltage set of the modular multilevel converter in the second control cycle by processing the first sub-module set and the first sub-module voltage set through a voltage sorting algorithm and an optimal sub-module selection algorithm; for details, please refer to the relevant description of step 102 in the above method embodiment.

[0076] The second processing module 403 is used to obtain a target predicted value set based on the voltage set of the second sub-module and the first current set through the modular multilevel converter prediction model; for details, please refer to the relevant description of step 103 in the above method embodiment.

[0077] The control module 404 is used to obtain the finite control set model predictive control result of the modular multilevel converter based on the target predicted value set and through the cost function control; for details, please refer to the relevant description of step 104 in the above method embodiment.

[0078] The modular multilevel converter finite control set model predictive control device provided in this embodiment of the invention reduces the number of selectable sub-modules in the modular multilevel converter and the computational load of finite control set model predictive control by using an optimal sub-module selection algorithm, while ensuring the output performance of the modular multilevel converter.

[0079] As an optional implementation of this invention, the first sub-module set in the acquisition module includes the number of first bridge leg conducting sub-modules, the number of first upper bridge arm conducting sub-modules, and the number of first lower bridge arm conducting sub-modules corresponding to the modular multilevel converter within the first control cycle; the first sub-module voltage set includes the voltage of each upper bridge arm sub-module and the voltage of each lower bridge arm sub-module in the modular multilevel converter within the first control cycle; the first current set includes the rectifier-side AC current value and the rectifier-side circulating current value of the modular multilevel converter within the second control cycle.

[0080] As an optional implementation of this invention, the first processing module includes: a first processing submodule, used to obtain, based on the number of the first bridge leg conducting submodules, the number of the first upper bridge arm conducting submodules, and the number of the first lower bridge arm conducting submodules, the number of the second upper bridge arm conducting submodules, and the number of the first lower bridge arm conducting submodules, the number of the second upper bridge arm conducting submodules, and the number of the first lower bridge arm conducting submodules, the number of the second upper bridge arm conducting submodules, and the number of the third lower bridge arm conducting submodules, through the optimal submodule selection algorithm, the number of the second upper bridge arm conducting submodules and the number of the third lower bridge arm conducting submodules; and a second processing submodule, used to obtain, based on the voltage of each upper bridge arm submodule and the voltage of each lower bridge arm submodule, the number of the second upper bridge arm conducting submodules and the number of the third lower bridge arm conducting submodules ... The voltage is processed by the voltage sorting algorithm to obtain the voltage matrix of the upper arm submodule and the voltage matrix of the lower arm submodule; a first determining submodule is used to determine the voltage set of the upper arm conducting submodule and the voltage set of the lower arm conducting submodule based on the number of the second upper arm conducting submodule, the number of the second lower arm conducting submodule, the voltage matrix of the upper arm submodule, and the voltage matrix of the lower arm submodule; a second determining submodule is used to determine the voltage set of the second submodule based on the voltage set of the upper arm conducting submodule and the voltage set of the lower arm conducting submodule.

[0081] As an optional implementation of this invention, the first determining submodule includes: a third determining submodule, configured to determine the upper arm conducting submodule voltage set in the upper arm submodule voltage matrix based on the number of the second upper arm conducting submodules; and a fourth determining submodule, configured to determine the lower arm conducting submodule voltage set in the lower arm submodule voltage matrix based on the number of the second lower arm conducting submodules.

[0082] As an optional implementation of this invention, the acquisition module includes: an acquisition submodule, used to acquire a second current set of the modular multilevel converter during the second control cycle, the second current set reflecting the current reference value of the modular multilevel converter; and an establishment submodule, used to establish the cost function based on the second current set and the target predicted value set.

[0083] As an optional implementation of this invention, the control module includes: a fifth determining submodule, used to determine a target conduction submodule based on the target predicted value set and the cost function; a sending submodule, used to send a conduction control signal to the target conduction submodule and a turn-off signal to other conduction submodules in the modular multilevel converter when the target conduction submodule is determined; and a control submodule, used to control the modular multilevel converter within the second control cycle based on the conduction control signal and the turn-off signal, and obtain the finite control set model predictive control result of the modular multilevel converter.

[0084] For a detailed description of the function of the modular multilevel converter finite control set model predictive control device provided in this embodiment of the invention, please refer to the description of the modular multilevel converter finite control set model predictive control method in the above embodiments.

[0085] This invention also provides a storage medium, such as... Figure 5 As shown, a computer program 501 is stored thereon. When executed by a processor, this program implements the steps of the modular multilevel converter finite control set model predictive control method described in the above embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0086] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0087] This invention also provides an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 61 and a memory 62, wherein the processor 61 and the memory 62 may be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0088] Processor 61 can be a central processing unit (CPU). Processor 61 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0089] The memory 62, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the corresponding program instructions / modules in the embodiments of the present invention. The processor 61 executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory 62, thereby implementing the modular multilevel converter finite control set model predictive control method in the above method embodiments.

[0090] The memory 62 may include a program storage area and a data storage area. The program storage area may store applications required for operating the device and at least one function; the data storage area may store data created by the processor 61, etc. Furthermore, the memory 62 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 62 may optionally include memory remotely located relative to the processor 61, and these remote memories may be connected to the processor 61 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0091] The one or more modules are stored in the memory 62, and when executed by the processor 61, they perform the following: Figure 1-3 The modular multilevel converter finite control set model predictive control method in the illustrated embodiment.

[0092] For specific details regarding the aforementioned electronic devices, please refer to the relevant documentation. Figures 1 to 3 The relevant descriptions and effects in the illustrated embodiments are for understanding purposes only and will not be repeated here.

[0093] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A modular multilevel converter finite control set model predictive control method, characterized in that, The method comprises: obtaining a cost function, a modular multilevel converter prediction model, a first sub-module set of the modular multilevel converter in a first control period, a first sub-module voltage set and a first current set of the modular multilevel converter in a second control period adjacent to the first control period; based on the first sub-module set and the first sub-module voltage set, processing through a voltage sorting algorithm and an optimal sub-module selection algorithm to obtain a second sub-module voltage set of the modular multilevel converter in the second control period; based on the second sub-module voltage set and the first current set, processing through the modular multilevel converter prediction model to obtain a target prediction value set; based on the target prediction value set, processing through the cost function control to obtain a limited control set model prediction control result of the modular multilevel converter; wherein the cost function is represented by the following relationship: In the formula: represents a cost function; , , all represent coefficients of the cost function; represents the j-phase reference value of the AC side current in the j-th control period; k represents the j-phase reference value of the AC side current in the j-th control period; represents the j-phase reference value of the AC side current in the j-th control period; k represents the j-phase reference value of the AC side current in the j-th control period; represents the upper bridge arm energy prediction value; represents the upper bridge arm energy prediction value; represents the j-phase reference value of the AC side current in the j-th control period; k+ 1 control period of the modular multilevel converter; represents the j-phase reference value of the AC side current in the j-th control period; k+ 1 control period of the modular multilevel converter; represents the j-phase reference value of the AC side current in the j-th control period; k+ 1 control period of the modular multilevel converter; The modular multilevel converter prediction model is used to predict the AC current value, the circulating current value and the bridge arm energy value of the modular multilevel converter through the following relationship: In the formula: denotes the k+ 1 AC prediction value of the modular multilevel converter in a control period; denotes the control period; denotes the bridge arm inductance; denotes the bridge arm resistance that can exist; denotes the k+ 1 reference value of the DC bus voltage in a control period; denotes the k+ 1 voltage value of the upper bridge arm in the modular multilevel converter in a control period; denotes the k+ 1 voltage value of the lower bridge arm in the modular multilevel converter in a control period; denotes the rectifier-side AC current value of the modular multilevel converter obtained by sampling; In the formula: denotes the first k+ 1 control cycle of the modular multilevel converter; denotes the first k+ 1 control cycle of the j-phase AC side voltage reference value; denotes the rectifier side circulating current value of the modular multilevel converter sampled; In the formula: represents the number of sub-modules in the bridge arm of the modular multilevel converter in the first control period; k+ 1control period module multilevel converter bridge arm energy prediction value; represents the number of sub-modules in the bridge arm of the modular multilevel converter in the first control period; represents the number of sub-modules in the bridge arm of the modular multilevel converter in the first control period; represents the number of sub-modules in the bridge arm of the modular multilevel converter in the first control period; k+ 1control period module multilevel converter bridge arm or lower arm input sub-module voltage and.

2. The method of claim 1, wherein the first sub-module set includes the number of first bridge leg conducting sub-modules, the number of first upper bridge arm conducting sub-modules and the number of first lower bridge arm conducting sub-modules of the modular multilevel converter in the first control period; the first sub-module voltage set includes the voltage of each upper bridge arm sub-module and the voltage of each lower bridge arm sub-module of the modular multilevel converter in the first control period; the first current set includes the AC current value and the circulating current value of the rectifier side of the modular multilevel converter in the second control period.

3. The method of claim 2, wherein, Based on the first sub-module set and the first sub-module voltage set, processing through a voltage sorting algorithm and an optimal sub-module selection algorithm to obtain a second sub-module voltage set of the modular multilevel converter in the second control period, comprising: based on the number of first bridge leg conducting sub-modules, the number of first upper bridge arm conducting sub-modules and the number of first lower bridge arm conducting sub-modules, processing through the optimal sub-module selection algorithm to obtain the number of second upper bridge arm conducting sub-modules and the number of second lower bridge arm conducting sub-modules of the modular multilevel converter in the second control period; based on the voltage of each upper bridge arm sub-module and the voltage of each lower bridge arm sub-module, processing through the voltage sorting algorithm to obtain an upper bridge arm sub-module voltage matrix and a lower bridge arm sub-module voltage matrix; based on the number of second upper bridge arm conducting sub-modules, the number of second lower bridge arm conducting sub-modules, the upper bridge arm sub-module voltage matrix and the lower bridge arm sub-module voltage matrix, determining an upper bridge arm conducting sub-module voltage set and a lower bridge arm conducting sub-module voltage set; based on the upper bridge arm conducting sub-module voltage set and the lower bridge arm conducting sub-module voltage set, determining the second sub-module voltage set.

4. The method of claim 3, wherein, The determining the upper bridge arm conduction submodule voltage set and the lower bridge arm conduction submodule voltage set based on the second upper bridge arm conduction submodule number, the second lower bridge arm conduction submodule number, the upper bridge arm submodule voltage matrix and the lower bridge arm submodule voltage matrix comprises: The determining the upper bridge arm conduction submodule voltage set based on the second upper bridge arm conduction submodule number in the upper bridge arm submodule voltage matrix; The determining the lower bridge arm conduction submodule voltage set based on the second lower bridge arm conduction submodule number in the lower bridge arm submodule voltage matrix.

5. The method of claim 1, wherein, The cost function is obtained, comprising: The second current set of the modular multilevel converter in the second control period is obtained, and the second current set reflects the current reference value of the modular multilevel converter; The cost function is established based on the second current set and the target prediction value set.

6. The method of claim 5, wherein, The finite control set model predictive control result of the modular multilevel converter is obtained through the cost function control based on the target prediction value set, comprising: The target conduction submodule is determined based on the target prediction value set through the cost function; When the target conduction submodule is determined, the modular multilevel converter sends a conduction control signal to the target conduction submodule, and sends an off signal to other conduction submodules in the modular multilevel converter; The modular multilevel converter is controlled in the second control period based on the conduction control signal and the off signal, and the finite control set model predictive control result of the modular multilevel converter is obtained.

7. A modular multilevel converter finite control set model predictive control device, characterized in that, The device comprises: The acquisition module is used to obtain a cost function, a modular multilevel converter prediction model, a first submodule set of the modular multilevel converter in a first control period, a first submodule voltage set and a first current set of the modular multilevel converter in a second control period, and the second control period is the next control period adjacent to the first control period; The first processing module is used to obtain a second submodule voltage set of the modular multilevel converter in the second control period through a voltage sorting algorithm and an optimal submodule selection algorithm based on the first submodule set and the first submodule voltage set; The second processing module is used to obtain a target prediction value set through the modular multilevel converter prediction model based on the second submodule voltage set and the first current set; The control module is used to obtain a finite control set model predictive control result of the modular multilevel converter through the cost function control based on the target prediction value set. The cost function is represented by the following relationship: In the formula: Represents the cost function; , , Each represents a coefficient of the cost function; Indicates the first k Reference value of the AC side current of phase j within the +1 control cycle; Indicates the first k Reference value of the j-phase circulating current side current within the +1 control cycle; This indicates the predicted energy value of the upper bridge arm; This indicates the predicted energy value of the upper bridge arm; Indicates the first k+ Predicted circulating current of a modular multilevel converter within a control cycle; Indicates the first k+ 1. AC power prediction value of modular multilevel converter within the control cycle; Indicates the first k+ Predicted arm energy of a modular multilevel converter during a control cycle; The modular multilevel converter prediction model is used to predict the AC current value, the circulating current value and the bridge arm energy value of the modular multilevel converter through the following relationship respectively: In the formula: denotes the k+ 1 AC voltage prediction value of the modular multilevel converter within a control period; denotes the control period; denotes the bridge arm inductance; denotes the bridge arm resistance, if present; denotes the k+ 1 reference value of the DC bus voltage within a control period; denotes the k+ 1 voltage value of the upper bridge arm in the modular multilevel converter within a control period; denotes the k+ 1 voltage value of the lower bridge arm in the modular multilevel converter within a control period; denotes the rectifier-side AC current value of the modular multilevel converter obtained by sampling; In the formula: denotes the first k+ 1 control cycle of the modular multilevel converter; denotes the first k+ 1 control cycle of the j-phase AC side voltage reference value; denotes the sampling obtained rectifier side circulating current value of the modular multilevel converter; In the formula: represents the first k+ 1 control cycle of the bridge arm energy prediction value of the modular multilevel converter; represents the capacitor value of the sub-module in the modular multilevel converter; represents the number of sub-modules of the bridge arm in the modular multilevel converter; represents the first k+ 1 control cycle of the voltage and of the upper bridge arm or the lower bridge arm into the sub-module.

8. The apparatus of claim 7, wherein, In the acquisition module, The first submodule set comprises the first bridge leg conduction submodule number, the first upper bridge arm conduction submodule number and the first lower bridge arm conduction submodule number corresponding to the modular multilevel converter in the first control period; The first sub-module voltage set comprises a voltage of each upper bridge arm sub-module and a voltage of each lower bridge arm sub-module in the modular multilevel converter in the first control period; The first current set comprises a rectifier side alternating current value and a rectifier side circulating current value of the modular multilevel converter in the second control period.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is used to make the computer execute the modular multilevel converter finite control set model predictive control method in any one of claims 1 to 6.

10. An electronic device, comprising: Comprise: A memory and a processor, which are in communication connection with each other, the memory stores a computer program, and the processor executes the modular multilevel converter finite control set model predictive control method in any one of claims 1 to 6 by executing the computer program.

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