Predictive control method and device for a power converter

Abstract

This application provides a predictive control method and apparatus for a converter. The predictive control method includes: obtaining a reference value for the AC side current of a cascaded H-bridge converter; obtaining the total disturbance observation value of the cascaded H-bridge converter in the current cycle and inputting it into a hyperlocal model (HLM); the HLM outputs a predicted value for the AC side current in the next cycle based on the total disturbance observation value and the actual value of the AC side current of the cascaded H-bridge converter in the current cycle; inputting the AC side current reference value and the predicted value for the AC side current in the next cycle into a model predictive control (MPC); and the switch vector selector in the MPC determining the target switch combination for the next cycle from candidate switch state combinations in the cascaded H-bridge converter based on a calculated cost function. Even when the mathematical model in the MPC may mismatch, this predictive control method can accurately output the predicted value for the AC side current in the next cycle to select the target switch combination that can compensate for external disturbances in the next cycle.

Description

Technical Field

[0001] This application relates to the field of power electronic converter control technology, and in particular to a predictive control method and device for a converter. Background Technology

[0002] Cascaded H-Bridge (CHB) converters, as a multi-level topology, are widely used in power electronic transformers, electric traction systems, and new energy grid connection due to their advantages such as high modularity, good output waveform quality, and low stress on switching devices.

[0003] Predictive control of cascaded H-bridge converters can be achieved using the Model Predictive Control (MPC) method in related technologies, including using MPC to predict the AC side current for the next cycle to select the optimal switching combination.

[0004] However, the MPC method relies entirely on an accurate system mathematical model. In some cases involving cascaded H-bridge converters (such as when there are external disturbances or when there are parameter changes within the cascaded H-bridge converter), the system mathematical model in MPC becomes mismatched due to not being updated in time. This results in inaccurate predictions of the AC side current for the next cycle, which in turn deteriorates the predictive control performance of the converter and increases the steady-state error. Summary of the Invention

[0005] This application provides a predictive control method for a converter to solve the problem that the predictive control of cascaded H-bridge converters in related technologies relies entirely on MPC.

[0006] To solve the above-mentioned technical problems, this application is implemented as follows: Firstly, a predictive control method for a converter is provided, the method comprising: Obtain the reference value of the AC side current of the cascaded H-bridge converter; The total disturbance observation value of the cascaded H-bridge converter in the current cycle is obtained and input into the hyperlocal model. The hyperlocal model outputs the predicted value of the AC side current in the next cycle based on the total disturbance observation value in the current cycle and the actual value of the AC side current of the cascaded H-bridge converter in the current cycle. The AC side current reference value and the AC side current prediction value for the next cycle are input into the model predictive control. The switch vector selector in the model predictive control determines the target switch combination for the next cycle from each candidate switch state combination in the cascaded H-bridge converter based on the calculation cost function.

[0007] Secondly, a predictive control device for a converter is provided, comprising: The current reference value acquisition module is used to acquire the AC side current reference value of the cascaded H-bridge converter; The current prediction output module is used to obtain the total disturbance observation value of the cascaded H-bridge converter in the current cycle and input it into the hyperlocal model. The hyperlocal model outputs the AC side current prediction value for the next cycle based on the total disturbance observation value in the current cycle and the actual AC side current value of the cascaded H-bridge converter in the current cycle. The target combination determination module is used to input the AC side current reference value and the AC side current prediction value for the next cycle into the model predictive control. The switch vector selector in the model predictive control determines the target switch combination for the next cycle from each candidate switch state combination in the cascaded H-bridge converter based on the calculation cost function.

[0008] This application provides a predictive control method for a converter. It obtains the total disturbance observation value of a cascaded H-bridge converter in the current cycle. Based on this total disturbance observation value and the actual AC-side current value of the cascaded H-bridge converter in the current cycle, a hyperlocal model outputs a predicted AC-side current value for the next cycle. This predicted AC-side current value and an AC-side current reference value for the next cycle are then input into the model predictive control (MPC). The switch vector selector in the MPC determines the target switch combination for the next cycle based on a calculated cost function. Therefore, in this embodiment, the predictive control method for the cascaded H-bridge converter does not rely entirely on model predictive control (MPC). Instead, it is based on the total disturbance observation value of the current cycle, and the hyperlocal model outputs the predicted AC-side current value for the next cycle based on the total disturbance observation value and the actual AC-side current value of the cascaded H-bridge converter in the current cycle. Thus, even when the mathematical model in the MPC may mismatch, this predictive control method can still output a more accurate predicted AC-side current value for the next cycle, allowing for the selection of a target switch combination that can compensate for external disturbances in the next cycle, achieving good control and minimal steady-state error in the next cycle. Compared to related technologies, the predictive control method of this application solves the problem that the predictive control of cascaded H-bridge converters in related technologies relies entirely on MPC. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in one or more embodiments of this specification, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating the predictive control method for a converter provided in an embodiment of this application; Figure 2 A control system block diagram of the predictive control method for a converter provided in an embodiment of this application; Figure 3 The structural block diagram of MELSO in the predictive control method for converters provided in the embodiments of this application; Figure 4 Another flowchart illustrating the predictive control method for a converter provided in this application embodiment; Figure 5 A schematic diagram of the main circuit topology of the cascaded H-bridge converter in the predictive control method for the converter provided in the embodiments of this application; Figure 6 A schematic diagram of a predictive control device for a converter provided in the application embodiment; Figure 7 A schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0011] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0012] Cascaded H-Bridge (CHB) converters, as a multi-level topology, are widely used in power electronic transformers, electric traction systems, and new energy grid connection due to their advantages such as high modularity, good output waveform quality, and low stress on switching devices.

[0013] Predictive control of cascaded H-bridge converters can be achieved using the Model Predictive Control (MPC) method in related technologies, including using MPC to predict the AC side current for the next cycle to select the optimal switching combination.

[0014] However, the MPC method relies entirely on an accurate system mathematical model. In some cases involving cascaded H-bridge converters (such as when there are external disturbances, internal parameter changes in the cascaded H-bridge converter, or when the MPC model is not established), the system mathematical model in MPC becomes mismatched due to not being updated in time. This results in inaccurate predictions of the AC side current for the next cycle, which in turn deteriorates the predictive control performance of the converter and increases the steady-state error.

[0015] To address the aforementioned problems, one or more embodiments of a predictive control method for a converter provided in this application are as follows: Reference Figure 1 This illustrates a predictive control method for a converter provided in an embodiment of this application, which may include the following steps S102 to S106. Corresponding to... Figure 2 The present application provides a block diagram of a predictive control method for a converter according to an embodiment of the present application. Figure 2 middle This refers to the AC side voltage (or grid side voltage). This refers to the AC side current (or grid side current). For filtering inductors, Parasitic resistance, , and The actual DC-side voltage values ​​of the three units respectively.

[0016] Step S102: Obtain the AC side current reference value of the cascaded H-bridge converter 10. .

[0017] Step S104: Obtain the total disturbance observation value of the cascaded H-bridge converter 10 in the current period. The data is then input into the hyperlocal model 20, which is based on the total disturbance observation value for the current period. The actual value of the AC side current of the cascaded H-bridge converter in the current cycle. Output the predicted AC side current value for the next cycle. The total disturbance observation value This includes observations of external disturbances and / or observations of internal disturbances caused by changes in the internal parameters of the cascaded H-bridge converter. The hyperlocal model 20 can be pre-built.

[0018] Step S106: Set the AC side current reference value Predicted AC current value for the next cycle The input is fed into the model predictive control 30, where the switch vector selector determines the target switch combination for the next cycle from each candidate switch state combination in the cascaded H-bridge converter 10 based on the calculated cost function.

[0019] This application provides a predictive control method for a converter, which obtains the total disturbance observation value of a cascaded H-bridge converter 10 in the current period. In the hyperlocal model 20, based on this total perturbation observation value The actual value of the AC side current of the current cascaded H-bridge converter. Output the predicted AC side current value for the next cycle. Then, the predicted AC side current value for the next cycle is... AC side current reference value The input is fed into the model predictive control 30, where the switch vector selector determines the target switch combination for the next cycle based on a calculated cost function. Therefore, in this embodiment, the predictive control method for the cascaded H-bridge converter does not rely entirely on model predictive control (MPC), but instead incorporates the total disturbance observation for the current cycle. This is performed by the hyperlocal model 20 based on the total perturbation observations. The actual value of the AC side current of the current cascaded H-bridge converter. Output the predicted AC side current value for the next cycle. Therefore, even when the mathematical model in MPC is mismatched, this predictive control method can still output a more accurate predicted value of the AC side current for the next cycle. This allows for the selection of the target switching combination that can compensate for external disturbances in the next cycle, achieving good control and a smaller steady-state error in the next cycle. Compared to related technologies, the predictive control method in this application solves the problem that the predictive control of cascaded H-bridge converters in related technologies relies entirely on MPC.

[0020] The cost calculation function can be used to evaluate the difference between the current performance of each candidate switch state combination of the cascaded H-bridge converter and the converter's target performance (or ideal performance) in each control cycle. The switch vector selector (or switch vector selection) refers to selecting the target switch combination from all possible switch state combinations in each control cycle based on the evaluation results of the cost calculation function. This target switch combination is the optimal set of switch signals among all possible switch state combinations, and the performance achievable when driving the cascaded H-bridge converter is closest to the converter's target performance; that is, the achievable performance is optimal. The converter can be... Figure 2 Taking a three-unit cascaded H-bridge converter as an example, each H-bridge unit has 4 switches, and the switch combinations for each H-bridge unit are (1,0)(0,0)(1,1)(0,1), which can output three voltage levels: +1, 0, and -1. Therefore, the total number of combinations is 3. 3 =27 types, from which the optimal switch combination can be selected based on the cost function.

[0021] In one possible embodiment, the cost calculation function is to calculate the cost J for each candidate switch vector, and the optimal set of switch signals refers to the set of switch signals with the lowest cost J among all possible switch combinations.

[0022] In one possible implementation, after step S106 above, step S108 may also be included: applying the optimal switch combination to the cascaded H-bridge converter in the next cycle.

[0023] In one implementation, the total disturbance observation value of the current period in step S104 above It can be observed by (e.g.) Figure 2 The output of the MLESO observer 40 is the total disturbance observation value for the current period. In the hyperlocal model 20.

[0024] The observer 40 can be a Modified Linear Extended State Observer (MLESO) or another type of observer. (See reference...) Figure 3 The observer 40 can be based on the actual value of the AC side current for the current period collected by the sensor. Current AC side current observation value for the current cycle Calculate and output the total disturbance observation for the current period. .

[0025] In this embodiment of the application, the reference Figure 3 The above step S104 may include steps A1, A2, A3 and A4.

[0026] Step A1: Obtain the actual value of the AC side current of the cascaded H-bridge converter in the current cycle. The actual value of the AC side current can be obtained by a sensor.

[0027] Step A2: Obtain the AC side current observation value for the current cycle output by the observer. .

[0028] Step A3: Based on the actual value of the AC side current With the AC side current observation value Determine the current observation error in the current cycle. This means determining the deviation between the actual value and the observer's observed value.

[0029] Step A4: Based on the current cycle current observation error Sampling period The properties of the observer determine the total disturbance observation value for the current period. .

[0030] Among them, sampling period This refers to the adjustment period of the switching combination within the cascaded H-bridge converter, and its value can be between 50μs and 200μs.

[0031] The observer's properties may include the first error feedback gain in the observer. Second error feedback gain The first error feedback gain Second error feedback gain This can be achieved through pole configuration (given the observer bandwidth). The observer gain is calculated. In this embodiment, the first feedback gain of MLESO is... It can be equal to the second feedback gain. And all are equal to the observer bandwidth. This leads to more stable predictive control. In related technologies, the error feedback gain of the observer is the square of the observer bandwidth, which is... In the embodiments of this application, the first error feedback gain of the observer... Second error feedback gain All are reduced to equal the observer's bandwidth. Thus, it can be seen that the observer 40 (i.e., MLESO) of this application can utilize the error signal more efficiently, thereby significantly reducing the feedback gain and maintaining better performance.

[0032] In one implementation, the reference Figure 3 Step A4 above may include steps B1, B2, B3, B4 and B5.

[0033] Step B1: Based on the current cycle current observation error Combined with unit delay operator Determine the current observation error of the previous cycle. .

[0034] Step B2: Based on the current observation error of the current cycle Combined with sampling period and the first error feedback gain of the observer The current error after determining the gain.

[0035] Step B3: Calculate the difference between the current error after gain and the current observation error of the previous cycle. Determine the error increment.

[0036] Step B4: The error increment is multiplied by the second error feedback gain of the observer. Determine the total disturbance observation for the next period. .

[0037] Step B5: Based on the total disturbance observation for the next period Combined with unit delay operator Determine the total disturbance observation value for the current period. .

[0038] In one implementation, steps B1 to B5 described above can be performed by the MLESO observer 40.

[0039] Among them, the unit delay operator This refers to a signal delay of one sampling period.

[0040] In this embodiment of the application, combined with Figure 2 The above step S02 may include steps C1, C2 and C3.

[0041] Step C1: Obtain the reference value of the DC side voltage of the cascaded H-bridge converter 10. Actual value of DC side voltage The voltage difference. Wherein, the actual value of the DC side voltage... The number n in the equation is determined by the number of H-bridge units, for example... Figure 2 The diagram shows three H-bridge units; therefore, the actual value of the DC-side voltage is... It can include , and .

[0042] Step C2: Adjust the voltage difference according to the proportional-integral (PI) controller and output the current amplitude command.

[0043] Step C3: The phase-locked loop (PLL) determines the voltage based on the AC side voltage. Power grid phase information And the current amplitude command, output AC side current reference value. .

[0044] Reference Figure 4 This illustrates another predictive control method for a converter provided in an embodiment of this application, which may include the following steps S402 to S406, corresponding to... Figure 2 The present application provides a block diagram of a predictive control method for a converter according to an embodiment of the present application.

[0045] Step S402: Obtain the AC side current reference value of the cascaded H-bridge converter. .

[0046] Step S404: Obtain the total disturbance observation value of the cascaded H-bridge converter in the current period. The input voltage is then fed into a hyperlocal model, which is based on the input voltage of the cascaded H-bridge converter. Sampling period The state gain of the hyperlocal model The total disturbance observation value The actual value of the AC side current of the cascaded H-bridge converter in the current cycle. Determine the predicted value of the AC side current for the next cycle. The total disturbance observation value This includes observations of external disturbances and / or observations of internal disturbances caused by changes in the internal parameters of the cascaded H-bridge converter.

[0047] Step S406: Set the AC side current reference value Predicted AC current value for the next cycle The input is fed into the model predictive control 30, where the switch vector selector determines the optimal switch combination for the next cycle from each candidate switch state combination in the cascaded H-bridge converter 10 based on a calculated cost function.

[0048] In this embodiment of the application, the input voltage of the cascaded H-bridge converter in step S404 It is the input voltage to the AC side of the cascaded H-bridge converter, such as Figure 5 The diagram shows the main circuit topology of a cascaded H-bridge converter, where... This refers to the AC side voltage (or grid side voltage). This refers to the AC side current (or grid side current). This is the input voltage of the converter, which is the sum of the input voltages of the n H-bridge units. , These represent the DC-side voltage and current of the nth H-bridge unit, respectively. This cascaded H-bridge converter consists of n single-phase H-bridge units cascaded together. Each H-bridge unit, by controlling its four internal switches, can generate three output level states: + ,0,- .

[0049] In this embodiment of the application, step S302 can refer to step S102 above, and step S306 can refer to step S106 above, and will not be repeated here.

[0050] In one possible implementation, in the hyperlocal model 20, the parameters Figure 2 We can know the following formula: , In the above formula, For state gain, The input voltage of the converter. For the total disturbance observations, This is the actual value of the AC side current. This is the predicted value of the AC side current for the next cycle.

[0051] In one implementation, the hyperlocal model 20 is a system dynamic description method for model-free control strategies. It can describe the system state using only the system's inputs and outputs, without relying on the system's specific physical parameters, such as resistance, inductance, and capacitance. It represents the unmodeled dynamics, parameter mismatch, and external disturbances in the system as a lumped disturbance, which can simplify a complex system.

[0052] In one possible implementation, to improve the system's resilience to parameter disturbances and external interference, this application embodiment constructs a hyperlocal model of the AC side current and represents disturbances and external interferences in the CHB converter, including but not limited to those caused by parameter mismatch, as F. The established model is as follows: , In the formula, For AC side current, The input voltage of the converter, and the state gain of the hyperlocal model. The MLESO observer 40 can treat this as one of the states in the system, thereby establishing a new state space. By observing the new state space, the observed values ​​of the uncertainties can be obtained, and the observed values ​​of the total disturbance mentioned above can be output. .

[0053] In one possible implementation, the state-space expression of the MLESO hyperlocal model in the discrete domain of this application is as follows: , Wherein, the above expression and Figure 3 The block diagram of MLESO in the discrete domain shown corresponds to the one in the expression. Figure 3 The parameters shown are also corresponding, and will not be repeated here.

[0054] In this embodiment, MLESO can observe system disturbances in real time and feedforward the total disturbance to the model predictive control, forming an effective disturbance prediction mechanism and significantly improving the robustness of the system. In this embodiment, by introducing the MLESO observer, system disturbances can be accurately and quickly estimated and compensated, effectively suppressing the impact of parameter uncertainty, unmodeled dynamics and external disturbances on the performance of the CHB system, and significantly improving the stable operation capability of the cascaded H-bridge converter under complex operating conditions.

[0055] In this embodiment, the MLESO observer 40 achieves faster state tracking speed and smaller steady-state observation error by optimizing the error feedback mechanism. Combined with the rolling optimization characteristics of the model predictive control 30, the system has both excellent dynamic response speed and steady-state control accuracy.

[0056] In this embodiment, the inclusion of a hyperlocal model 20 reduces the reliance on the precise mathematical model of the model predictive control 30, enhancing the universality of the control strategy and facilitating its application on cascaded H-bridge converters of different models or parameters. Because the MLESO structure is clear, parameter tuning has explicit theoretical guidance (such as pole placement), computational complexity is relatively low, and it is easy to implement on a digital control platform, demonstrating promising engineering application prospects.

[0057] This application also provides a converter predictive control device, which is capable of executing the steps of the above-described converter predictive control method. Figure 6 The converter predictive control device 60 includes: The current reference value acquisition module 61 is used to acquire the AC side current reference value of the cascaded H-bridge converter. The current prediction output module 62 is used to obtain the total disturbance observation value of the cascaded H-bridge converter in the current cycle and input it into the hyperlocal model. The hyperlocal model outputs the AC side current prediction value for the next cycle based on the total disturbance observation value in the current cycle and the actual AC side current value of the cascaded H-bridge converter in the current cycle. The optimal combination determination module 63 is used to input the AC side current reference value and the AC side current prediction value for the next cycle into the model predictive control. The switch vector selector in the model predictive control determines the optimal switch combination for the next cycle from each candidate switch state combination in the cascaded H-bridge converter based on the calculation cost function.

[0058] In one implementation, the current prediction output module 62 acquires the total disturbance observation value of the cascaded H-bridge converter in the current cycle, including: Obtain the actual value of the AC side current of the cascaded H-bridge converter in the current cycle; Obtain the AC side current observation value for the current cycle output by the observer; The current observation error for the current cycle is determined based on the actual value of the AC side current and the observed value of the AC side current. The total disturbance observation value for the current period is determined based on the current observation error of the current period, the sampling period, and the properties of the observer.

[0059] In one implementation, determining the total disturbance observation value for the current period based on the current observation error, the sampling period, and the properties of the observer includes: Based on the current cycle current observation error f and the unit delay operator, determine the previous cycle current observation error. Based on the current observation error of the current in the current cycle, combined with the sampling period and the first error feedback gain of the observer, the current error after gain is determined; The difference between the current error after gain and the current observation error of the previous cycle is used to determine the error increment; The error increment is multiplied by the second error feedback gain of the observer to determine the total perturbation observation value for the next period. Based on the total disturbance observations for the next period, and in conjunction with the unit delay operator, the total disturbance observations for the current period are determined.

[0060] In one implementation, the current prediction output module 62 outputs a predicted AC current value for the next cycle based on the total disturbance observation value for the current cycle and the actual AC side current value of the cascaded H-bridge converter for the current cycle, including: Based on the input voltage of the cascaded H-bridge converter, the sampling period, the state gain of the hyperlocal model, the total disturbance observation, and the actual AC side current value of the cascaded H-bridge converter in the current period, the predicted AC side current value for the next period is determined.

[0061] In one embodiment, the current reference value acquisition module 61 acquires the AC side current reference value of the cascaded H-bridge converter, including: Obtain the voltage difference between the reference value and the actual value of the DC-side voltage of the cascaded H-bridge converter; The voltage difference is adjusted according to the proportional-integral controller, and a current amplitude command is output. The phase-locked loop outputs an AC side current reference value based on the grid phase information and the current amplitude command.

[0062] The converter predictive control device provided in this application embodiment can execute the methods described in the preceding method embodiments and achieve the functions and beneficial effects of the methods described in the preceding method embodiments, which will not be repeated here.

[0063] Figure 7 The diagram illustrates the hardware structure of an electronic device implementing the embodiments of this application. Referring to the diagram, at the hardware level, the electronic device includes a processor and optionally, an internal bus, a network interface, and a memory. The memory may include RAM, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk storage device. Of course, the electronic device may also include other hardware required for other services.

[0064] The processor, network interface, and memory can be interconnected via an internal bus, which can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be categorized as an address bus, data bus, control bus, etc. For ease of illustration, only a single bidirectional arrow is used in this diagram, but this does not imply that there is only one bus or one type of bus.

[0065] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0066] The processor reads the corresponding computer program from non-volatile memory into main memory and then executes it, forming a device at the logical level that locates the target user. The processor executes the program stored in memory and specifically performs the following: Figure 1 or Figure 4 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0067] The above is as stated in this application. Figure 1 or Figure 4The methods disclosed in the illustrated embodiments can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above methods can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0068] The electronic device can also execute the methods described in the preceding method embodiments and achieve the functions and beneficial effects of the methods described in the preceding method embodiments, which will not be repeated here.

[0069] Of course, in addition to software implementation, the electronic device of this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0070] This application also proposes a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform... Figure 1 or Figure 4 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0071] The computer-readable storage medium mentioned above includes read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc.

[0072] Furthermore, embodiments of this application also provide a computer program product, the computer program product including a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, which, when executed by a computer, implement the following process: Figure 1 or Figure 4 The methods disclosed in the embodiments shown achieve the functions and beneficial effects of the methods described in the preceding method embodiments, and will not be repeated here.

[0073] In summary, the above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

[0074] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0075] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0076] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0077] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

Claims

1. A method of predictive control of a power converter, characterized by, The method includes: Obtain the reference value of the AC side current of the cascaded H-bridge converter; The total disturbance observation value of the cascaded H-bridge converter in the current cycle is obtained and input into the hyperlocal model. The hyperlocal model outputs the predicted value of the AC side current in the next cycle based on the total disturbance observation value in the current cycle and the actual value of the AC side current of the cascaded H-bridge converter in the current cycle. The AC side current reference value and the AC side current prediction value for the next cycle are input into the model predictive control. The switch vector selector in the model predictive control determines the target switch combination for the next cycle from each candidate switch state combination in the cascaded H-bridge converter based on the calculation cost function.

2. The predictive control method of claim 1, wherein, The process of obtaining the total disturbance observation value of the cascaded H-bridge converter in the current period includes: Obtain the actual value of the AC side current of the cascaded H-bridge converter in the current period; Obtain the AC side current observation value for the current cycle output by the observer; The current observation error for the current cycle is determined based on the actual value of the AC side current and the observed value of the AC side current. The total disturbance observation value for the current period is determined based on the current observation error of the current period, the sampling period, and the properties of the observer.

3. The predictive control method of claim 2, wherein, The step of determining the total disturbance observation value for the current period based on the current observation error, sampling period, and the properties of the observer includes: Based on the current cycle current observation error, combined with the unit delay operator, determine the previous cycle current observation error; Based on the current observation error of the current in the current cycle, combined with the sampling period and the first error feedback gain of the observer, the current error after gain is determined; The difference between the current error after gain and the current observation error of the previous cycle is used to determine the error increment; The error increment is multiplied by the second error feedback gain of the observer to determine the total perturbation observation value for the next period. Based on the total disturbance observations for the next period, and in conjunction with the unit delay operator, the total disturbance observations for the current period are determined.

4. The predictive control method of claim 1, wherein, The hyperlocal model outputs a predicted value for the AC side current in the next cycle based on the observed total disturbance value and the actual AC side current value of the cascaded H-bridge converter in the current cycle, including: Based on the input voltage of the cascaded H-bridge converter, the sampling period, the state gain of the hyperlocal model, the total disturbance observation, and the actual AC side current value of the cascaded H-bridge converter in the current period, the predicted AC side current value for the next period is determined.

5. The predictive control method of claim 1, wherein, The process of obtaining the AC side current reference value of the cascaded H-bridge converter includes: Obtain the voltage difference between the reference value and the actual value of the DC-side voltage of the cascaded H-bridge converter; The voltage difference is adjusted according to the proportional-integral controller, and a current amplitude command is output. The phase-locked loop outputs an AC side current reference value based on the grid phase information and the current amplitude command.

6. A predictive control device of a converter, characterized by, include: The current reference value acquisition module is used to acquire the AC side current reference value of the cascaded H-bridge converter; The current prediction output module is used to obtain the total disturbance observation value of the cascaded H-bridge converter in the current cycle and input it into the hyperlocal model. The hyperlocal model outputs the AC side current prediction value for the next cycle based on the total disturbance observation value in the current cycle and the actual AC side current value of the cascaded H-bridge converter in the current cycle. The target combination determination module is used to input the AC side current reference value and the AC side current prediction value for the next cycle into the model predictive control. The switch vector selector in the model predictive control determines the target switch combination for the next cycle from each candidate switch state combination in the cascaded H-bridge converter based on the calculation cost function.

7. The predictive control device according to claim 6, characterized in that, The current prediction output module obtains the total disturbance observation value of the cascaded H-bridge converter in the current cycle, including: Obtain the actual value of the AC side current of the cascaded H-bridge converter in the current period; Obtain the AC side current observation value for the current cycle output by the observer; The current observation error for the current cycle is determined based on the actual value of the AC side current and the observed value of the AC side current. The total disturbance observation value for the current period is determined based on the current observation error of the current period, the sampling period, and the properties of the observer.

8. The predictive control device of claim 7, wherein, The step of determining the total disturbance observation value for the current period based on the current observation error, sampling period, and the observer's attributes includes: Based on the current cycle current observation error f and the unit delay operator, determine the previous cycle current observation error. Based on the current observation error of the current in the current cycle, combined with the sampling period and the first error feedback gain of the observer, the current error after gain is determined; The difference between the current error after gain and the current observation error of the previous cycle is used to determine the error increment; The error increment is multiplied by the second error feedback gain of the observer to determine the total perturbation observation value for the next period. Based on the total disturbance observations for the next period, and in conjunction with the unit delay operator, the total disturbance observations for the current period are determined.

9. The predictive control device of claim 6, wherein, In the current prediction output module, the hyperlocal model outputs the AC side current prediction value for the next period based on the total disturbance observation value for the current period and the actual AC side current value of the cascaded H-bridge converter for the current period, including: Based on the input voltage of the cascaded H-bridge converter, the sampling period, the state gain of the hyperlocal model, the total disturbance observation, and the actual AC side current value of the cascaded H-bridge converter in the current period, the predicted AC side current value for the next period is determined.

10. The predictive control device of claim 6, wherein, The current reference value acquisition module acquires the AC side current reference value of the cascaded H-bridge converter, including: Obtain the voltage difference between the reference value and the actual value of the DC-side voltage of the cascaded H-bridge converter; The voltage difference is adjusted according to the proportional-integral controller, and a current amplitude command is output. The phase-locked loop outputs an AC side current reference value based on the grid phase information and the current amplitude command.

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