A VIENNA rectifier model-free predictive current control method and system

Abstract

The invention discloses a VIENNA rectifier model-free predictive current control method and system, which belongs to the field of power electronic control. A super-local current model is first constructed to replace the VIENNA rectifier precise model, thereby reducing the dependence on model parameters; a cascade parallel extended state observer is then designed to estimate the lumped disturbance of the super-local model, thereby improving the system's anti-disturbance capability and measurement noise suppression capability; finally, a tolerance serial cost function is established, and a current term and a midpoint potential difference term are progressively evaluated. When the local optimal set of current terms flows to the midpoint potential difference term, a tolerance judgment is performed to obtain an optimal switching state, thereby realizing model-free predictive current control while eliminating weight factors.

Description

Technical Field

[0001] The invention belongs to the field of power electronic control, and in particular relates to a VIENNA rectifier model-free predictive current control method and system. Background Art

[0002] In today's industrial field, VIENNA rectifiers are widely used in electric vehicles, renewable energy systems, industrial power supplies and other fields due to their high efficiency and low harmonic distortion. However, under complex working conditions, the three-phase VIENNA rectifier is affected by factors such as line aging, temperature changes and sensor accuracy. The parameter disturbances and measurement noise generated make it difficult to establish an accurate prediction model. Traditional model predictive control strategies rely on accurate system models, and the resulting parameter mismatch reduces the control performance of the system. At the same time, due to its high gain characteristics, the traditional extended state observer is prone to amplify high-frequency measurement noise, resulting in reduced observation accuracy. In addition, traditional control strategies usually use a multi-objective cost function with weight factors to balance different control objectives. However, this cost function has the problem of difficulty in adjusting the weight factors under complex working conditions, which makes it difficult for the control system to maintain optimal performance under different working conditions. Summary of the invention

[0003] The present invention proposes a VIENNA rectifier model-free predictive current control method and system, which replaces the traditional extended state observer with a cascaded parallel extended state observer, further enhancing the system's anti-disturbance capability and improving the measurement noise suppression capability; and replaces the traditional multi-objective cost function containing weight factors with a tolerance serial cost function, which can eliminate the weight factors and save the weight factor setting process under complex working conditions, can meet the performance requirements under different application scenarios, and significantly improve the control performance of the system under complex working conditions.

[0004] The present invention provides a model-free predictive current control method for a VIENNA rectifier, comprising the following steps:

[0005] Step S1: reconstruct the three-phase inductor current prediction model according to the first-order super-local model to replace the original accurate model:

[0006] Step S2, designing a cascaded parallel extended state observer to estimate the lumped disturbance, wherein the first-stage observer of the cascade is used as a low-pass filter to filter out high-frequency measurement noise, and the second-stage observer adopts a parallel structure to estimate the lumped disturbance residual in different bandwidth frequency bands to suppress high-frequency measurement noise while resisting disturbance; a discrete current hyperlocal prediction model is obtained according to the estimated value of the cascaded parallel extended state observer;

[0007] Step S3, establish a tolerance serial cost function, and use the tolerance serial cost function to progressively evaluate the current term and the midpoint potential difference term to make a midpoint potential tolerance judgment. When the local optimal set of current terms flows to the midpoint potential difference term, a tolerance judgment is made to obtain the optimal switching state, thereby achieving model-free predictive current control while eliminating the weight factor.

[0008] The step S1 is specifically as follows:

[0009] (1) Establish the mathematical model of VIENNA rectifier:

[0010]

[0011] in, L is the AC side boost inductor; R is the equivalent resistance on the AC side; C is the DC bus capacitance; is the midpoint potential difference; is the inductor current on the AC side; The AC side inductor current, grid voltage, and terminal voltage are respectively dq Components in the coordinate system; is the angular frequency; They are the switch states of the bidirectional switch respectively;

[0012] (2) Reconstruct the three-phase inductor current prediction model based on the first-order hyperlocal model:

[0013] Considering the VIENNA rectifier control system as a single-input single-output system, the first-order hyperlocal model is constructed as follows:

[0014] (2)

[0015] in, x Represents the system status; is the derivative of x; α is the input gain; u is the control input; f is the lumped disturbance, which is the unknown part of the system; y is the output signal; To measure the effect of noise on the output signal;

[0016] Substituting the VIENNA rectifier current model in equation (1) into equation (2) yields the current hyperlocal model:

[0017]

[0018] in, For dq Control input gain in the coordinate system; is the lumped disturbance in dq Components in the coordinate system; and They are The derivative of

[0019] By using the forward Euler method to discretize equation (3), we can get k Discrete current hyperlocal prediction model for sampling periods:

[0020] (5)

[0021] in, is the system sampling period; , Indicates k Sampling period ; , Indicates k +1 sampling period ; , Indicates k Sampling period ; , Indicates k Sampling period ;

[0022] The forward Euler method is used to discretize equation (1) and we get k The discrete midpoint potential prediction model of sampling periods is: (6)

[0023] in, For the k + sampling period ; For the k Sampling period ; For the k Sampling period The matrix composed of For the k Sampling period The matrix composed of.

[0024] The step S2 is specifically as follows:

[0025] The expressions of the cascaded parallel extended state observer are:

[0026] (7)

[0027] in, For status at all levels; They are An estimated value of are the unknown disturbances at all levels; They are An estimated value of is the bandwidth of each level of the observer; according to the sub-frequency sequence of the cascaded parallel extended state observer , the design bandwidth parameter is ; ; ;

[0028] Substituting the current hyperlocal model of equation (3) into equation (7) yields:

[0029] (8)

[0030] (9)

[0031] The estimated value of the cascaded parallel extended state observer is obtained from equations (8) and (9): and :

[0032] (10)

[0033] Substituting the estimated value obtained by the cascaded parallel extended state observer into equation (5), the discrete current hyperlocal prediction model is obtained:

[0034] (11).

[0035] The step S3 is specifically as follows:

[0036] Traditional weighted factor The cost function is:

[0037] (12)

[0038] in, , The grid-side current is dq Reference values ​​of axis components, is the weight factor of the midpoint potential term;

[0039] Reconstruct the traditional cost function:

[0040] (13)

[0041] in, is the current term of the cost function, is the midpoint potential difference term of the cost function;

[0042] First evaluate the current term to minimize the current term cost function and obtain the index value of the alternative optimal switch state set:

[0043] (14)

[0044] in, Indicates n The cost function under the switching state, is the index value corresponding to the optimal switching state; and are respectively the alternative index values ​​in the set of alternative optimal switch states;

[0045] Then make tolerance for the midpoint potential difference Judgement, in Within the range, if the midpoint potential difference term corresponding to the alternative optimal switch state set All less than the tolerance , then the optimal index value is the one that minimizes the current term , otherwise the optimal index value is the one that minimizes the midpoint potential difference , specifically:

[0046] (15)

[0047] Select The switching state is the optimal switching state and is used as the output of predictive control to achieve model-free predictive current control.

[0048] A VIENNA rectifier model-free predictive current control system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned VIENNA rectifier model-free predictive current control method is implemented.

[0049] A computer storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned VIENNA rectifier model-free predictive current control method.

[0050] By adopting the technical solution of the present invention, firstly, a super-local current model is constructed to replace the VIENNA rectifier precise model, thereby reducing the dependence on model parameters; then, a cascaded parallel extended state observer is designed to estimate the lumped disturbance of the super-local model, thereby improving the anti-disturbance capability and measurement noise suppression capability of the system; finally, a tolerance serial cost function is established, and the current term and the midpoint potential difference term are progressively evaluated. When the local optimal set of current terms flows to the midpoint potential difference term, a tolerance judgment is performed to obtain the optimal switching state, thereby realizing model-free predictive current control while eliminating the weight factor.

[0051] The present invention has the following beneficial effects:

[0052] (1) A tolerance serial cost function is constructed to eliminate the weight factor and avoid the complex tuning process of the weight factor.

[0053] The cost function in the existing control strategy is usually a multi-objective function including current term and midpoint potential difference term, which contains weight factors that are difficult to adjust. The present invention reconstructs the cost function with a serial structure, eliminates the weight factors, and can better adapt to changes in working conditions; then, before the current term flows to the evaluation process of the midpoint potential difference term, a midpoint potential tolerance judgment step is added. In actual applications, the midpoint potential difference term tolerance is set according to performance requirements, which can better balance the performance of the current term and the midpoint potential difference term in the cost function.

[0054] (2) Based on the traditional extended state observer, a cascaded parallel extended state observer is designed to suppress measurement noise.

[0055] The observers used in traditional model-free predictive control methods are mostly extended state observers. Although the extended state observer can improve the system's anti-disturbance capability, its high gain characteristics easily amplify high-frequency measurement noise. Reducing the observer gain can reduce the sensitivity to high-frequency measurement noise, but it will reduce the tracking accuracy of the observer, so it is necessary to balance the estimation performance and noise sensitivity. The cascaded parallel extended state observer in the present invention, the first-stage observer of the cascade is used as a low-pass filter to filter out high-frequency measurement noise, and the second-stage observer adopts a parallel structure to estimate the lumped disturbance residual in different bandwidth frequency bands, which solves the problem of the difficulty in balancing the disturbance estimation performance and the measurement noise sensitivity, and achieves a better high-frequency measurement noise suppression effect while maintaining excellent anti-disturbance capability.

[0056] The present invention has significant advantages in the following aspects and has broad future development prospects:

[0057] 1) Efficient and stable power control: Efficient and stable current control is achieved without relying on an accurate model. The designed cascade parallel extended state observer provides excellent anti-disturbance capability and measurement noise suppression effect. This enables the system to maintain stable operation in the face of complex and changeable working conditions, improving the reliability and durability of the system.

[0058] 2) Flexible adaptation to different application scenarios: Model-free predictive control methods reduce the reliance on precise model parameters. The constructed tolerance cost function does not require the adjustment of weight factors and can adapt to complex and changing working conditions. The tolerance design enables the control strategy to meet the different performance requirements in different application scenarios. This feature makes it have a wide range of application potential in different application scenarios.

[0059] 3) Clean energy transformation: As the world pays more attention to and demands for renewable energy, VIENNA rectifiers, as core components of wind, solar, energy storage and microgrid systems, have a performance that directly affects energy efficiency and power system quality, providing technical support for the development of renewable energy, promoting energy transformation and reducing carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a control logic diagram of the present invention;

[0061] Figure 2 is a structural diagram of the cascaded parallel extended state observer in the present invention;

[0062] Figure 3 This is a flow chart of determining the midpoint potential tolerance using a tolerance serial cost function in the present invention. DETAILED DESCRIPTION

[0063] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0064] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0065] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0066] like Figure 1 As shown, the present invention provides a model-free predictive current control method for a VIENNA rectifier, comprising the following steps:

[0067] Step S1: reconstructing a three-phase inductor current prediction model according to a first-order super-local model to replace the original accurate model to reduce the dependence on model parameters:

[0068] (1) Establish the mathematical model of VIENNA rectifier:

[0069]

[0070] in, L is the AC side boost inductor; R is the equivalent resistance on the AC side; C is the DC bus capacitance; is the midpoint potential difference; is the inductor current on the AC side; The AC side inductor current, grid voltage, and terminal voltage are respectively dq Components in the coordinate system; is the angular frequency; They are the switch states of the bidirectional switch respectively;

[0071] (2) Reconstruct the three-phase inductor current prediction model based on the first-order hyperlocal model:

[0072] Considering the VIENNA rectifier control system as a single-input single-output system, the first-order hyperlocal model is constructed as follows:

[0073] (2)

[0074] in, x Represents the system status; is the derivative of x; α is the input gain; u is the control input; f is the lumped disturbance, which is the unknown part of the system; y is the output signal; To measure the effect of noise on the output signal;

[0075] Substituting the VIENNA rectifier current model in equation (1) into equation (2), we can obtain the current hyperlocal model:

[0076]

[0077] in, For dq Control input gain in the coordinate system; is the lumped disturbance in dq Components in the coordinate system; and They are The derivative of

[0078] By using the forward Euler method, we discretize equation (3) and get k Discrete current hyperlocal prediction model for sampling periods:

[0079] (5)

[0080] in, is the system sampling period; , Indicates k Sampling period ; , Indicates k +1 sampling period ; , Indicates k Sampling period ; , Indicates k Sampling period ;

[0081] By using the forward Euler method, we discretize equation (1) and get k The discrete midpoint potential prediction model of sampling periods is: (6)

[0082] in, For the k +1 sampling period ; For the k Sampling period ; For the k Sampling period The matrix composed of For the k Sampling period The matrix composed of

[0083] Step S2, design a cascaded parallel extended state observer to estimate the lumped disturbance, wherein the first-stage observer of the cascade is used as a low-pass filter to filter out high-frequency measurement noise, and the second-stage observer adopts a parallel structure to estimate the lumped disturbance residual in different bandwidth frequency bands, so as to achieve better high-frequency measurement noise suppression effect while maintaining excellent anti-disturbance capability, see Figure 2 :According to the estimated values ​​obtained by the cascaded parallel extended state observer, the discrete current hyperlocal prediction model is obtained:

[0084] (7)

[0085] in, For status at all levels; They are An estimated value of are the unknown disturbances at all levels; They are An estimated value of is the bandwidth of each level of the observer; according to the sub-frequency sequence of the cascaded parallel extended state observer , the design bandwidth parameter is ; ; ;

[0086] Substituting the current superlocal model of equation (3) into equation (7), we can obtain:

[0087] (8)

[0088] (9)

[0089] From equations (8) and (9), we can get the estimated value obtained by the cascade parallel extended state observer: and :

[0090] (10)

[0091] Since there is a one-beat delay between the actual output voltage and the reference value in a discrete control system, in order to compensate for the delay introduced by the calculation, the first k +2 sampling period prediction values ​​to achieve one-shot compensation for future errors;

[0092] Substituting the estimated value obtained by the cascaded parallel extended state observer into equation (5), the discrete current hyperlocal prediction model is obtained:

[0093] (11)

[0094] Step S4: Establish a tolerance serial cost function, and use the tolerance serial cost function to progressively evaluate the current term and the midpoint potential difference term to determine the midpoint potential tolerance, such as Figure 3 As shown in the figure, when the local optimal set of current terms flows to the midpoint potential difference term, a tolerance judgment is performed to obtain the optimal switching state, and model-free predictive current control is achieved without weighting factors:

[0095] In order to realize the controller's control of the AC side current and maintain the midpoint potential balance, the traditional The cost function is:

[0096] (12)

[0097] in, , The grid-side current is dq Reference values ​​of axis components, is the weight factor of the midpoint potential term;

[0098] Reconstruct the traditional cost function:

[0099] (13)

[0100] in, is the current term of the cost function, is the midpoint potential difference term of the cost function;

[0101] First evaluate the current term to minimize the current term cost function, and then obtain the index value of the alternative optimal switch state set:

[0102] (14)

[0103] in, Indicates n The cost function under the switching state, is the index value corresponding to the optimal switching state; and are respectively the alternative index values ​​in the set of alternative optimal switch states;

[0104] Then make tolerance for the midpoint potential difference Judgement, in Within the range, if the midpoint potential difference term corresponding to the alternative optimal switch state set All less than the tolerance , then the optimal index value is the one that minimizes the current term , otherwise the optimal index value is the one that minimizes the midpoint potential difference , as follows:

[0105] (15)

[0106] Select The switching state is the optimal switching state and is used as the output of predictive control to achieve model-free predictive current control.

[0107] Embodiment 2

[0108] The professionals should further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented by electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0109] Specifically, each step of the method embodiment in the embodiment of the present application can be completed by the hardware integrated logic circuit and / or software instructions in the processor, and the steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor, or a combination of hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps in the above method embodiment in combination with its hardware.

[0110] Embodiment 2 of the present invention provides a VIENNA rectifier model-free predictive current control system, which may include: a memory and a processor, the memory is used to store a computer program and transmit the program code to the processor. In other words, the processor can call and run the computer program from the memory to implement the method in Embodiment 1 of the present application. For example, the processor can be used to execute the above method embodiment according to the instructions in the computer program.

[0111] In some embodiments of the present application, the processor may include but is not limited to:

[0112] General-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.

[0113] In some embodiments of the present application, the memory includes, but is not limited to, volatile memory and / or non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (Direct Rambus RAM, DR RAM).

[0114] In some embodiments of the present application, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the method provided by the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0115] Embodiment 3

[0116] Embodiment 3 of the present invention further provides a computer storage medium on which a computer program is stored. When the computer program is executed by a computer, the computer is enabled to execute the method of the above method embodiment.

[0117] When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the first embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a solid state disk (solid state disk, SSD)), etc.

[0118] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A model-free predictive current control method for a VIENNA rectifier, characterized in that The following steps are involved: Step S1: reconstruct the three-phase inductor current prediction model according to the first-order super-local model to replace the original accurate model: Step S2, designing a cascaded parallel extended state observer to estimate the lumped disturbance, wherein the first-stage observer of the cascade is used as a low-pass filter to filter out high-frequency measurement noise, and the second-stage observer adopts a parallel structure to estimate the lumped disturbance residual in different bandwidth frequency bands to suppress high-frequency measurement noise while resisting disturbance; a discrete current hyperlocal prediction model is obtained according to the estimated value of the cascaded parallel extended state observer; Step S3, establishing a tolerance serial cost function, progressively evaluating the current term and the midpoint potential difference term through the tolerance serial cost function to make a midpoint potential tolerance judgment, and making a tolerance judgment when the local optimal set of current terms flows to the midpoint potential difference term to obtain the optimal switching state, and realizing model-free predictive current control under the condition of eliminating the weight factor; The step S1 is specifically as follows: (1) Establish the mathematical model of VIENNA rectifier: in, L is the AC side boost inductor; R is the equivalent resistance on the AC side; C is the DC bus capacitance; is the midpoint potential difference; is the inductor current on the AC side; The AC side inductor current, grid voltage, and terminal voltage are respectively dq Components in the coordinate system; is the angular frequency; They are the switch states of the bidirectional switch respectively; (2) Reconstruct the three-phase inductor current prediction model based on the first-order hyperlocal model: Considering the VIENNA rectifier control system as a single-input single-output system, the first-order hyperlocal model is constructed as follows: （2） in, x Represents the system status; for x The derivative of α is the input gain; u is the control input; f is the lumped disturbance, which is the unknown part of the system; y is the output signal; To measure the effect of noise on the output signal; Substituting the VIENNA rectifier current model in equation (1) into equation (2) yields the current hyperlocal model: in, For dq Control input gain in the coordinate system; is the lumped disturbance in dq Components in the coordinate system; and They are The derivative of By using the forward Euler method to discretize equation (3), we can get k Discrete current hyperlocal prediction model for sampling periods: （5） in, is the system sampling period; , Indicates k Sampling period ; , Indicates k +1 sampling period ; , Indicates k Sampling period ; , Indicates k Sampling period ; The forward Euler method is used to discretize equation (1) and we get k The discrete midpoint potential prediction model of sampling periods is: (6) in, For the k +1 sampling period ; For the k Sampling period ; For the k Sampling period The matrix composed of For the k Sampling period The matrix composed of.

2. A VIENNA rectifier model-free predictive current control method according to claim 1, characterized in that: The step S2 is specifically as follows: The expressions of the cascaded parallel extended state observer are: （7） in, For status at all levels; They are An estimated value of are the unknown disturbances at all levels; They are An estimated value of is the bandwidth of each level of the observer; according to the sub-frequency sequence of the cascaded parallel extended state observer , the design bandwidth parameter is ; ; ; Substituting the current hyperlocal model of equation (3) into equation (7) yields: （8） （9） The estimated value of the cascaded parallel extended state observer is obtained from equations (8) and (9): and : （10） Substituting the estimated value obtained by the cascaded parallel extended state observer into equation (5), the discrete current hyperlocal prediction model is obtained: （11）。 3. A VIENNA rectifier model-free predictive current control method according to claim 2, characterized in that: The step S3 is specifically as follows: Traditional weighted factor The cost function is: （12） in, , The grid-side current is dq Reference values ​​of axis components, is the weight factor of the midpoint potential term; Reconstruct the traditional cost function: （13） in, is the current term of the cost function, is the midpoint potential difference term of the cost function; First evaluate the current term to minimize the current term cost function and obtain the index value of the alternative optimal switch state set: （14） in, Indicates n The cost function under the switching state, is the index value corresponding to the optimal switching state; and are respectively the alternative index values ​​in the set of alternative optimal switch states; Then make tolerance for the midpoint potential difference Judgement, in Within the range, if the midpoint potential difference term corresponding to the alternative optimal switch state set All less than the tolerance , then the optimal index value is the one that minimizes the current term , otherwise the optimal index value is the one that minimizes the midpoint potential difference , specifically: （15） Select The switching state is the optimal switching state and is used as the output of predictive control to achieve model-free predictive current control.

4. A VIENNA rectifier model-free predictive current control system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, a VIENNA rectifier model-free predictive current control method according to any one of claims 1 to 3 is implemented.

5. A computer storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a VIENNA rectifier model-free predictive current control method according to any one of claims 1 to 3 is implemented.

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