Model prediction control method and system with error integration

Abstract

The invention provides a model predictive control method and system with error integration. The method and the system are used for a grid-side converter, and comprise the following steps: calculating instantaneous active power and reactive power of an output end of the grid-side converter according to a result obtained after Clark conversion is carried out on a power grid voltage and an output current; the output voltages of the space vectors corresponding to different switching states in the grid-side converter are converted into an alpha-beta coordinate system through Clark conversion; calculating the slope of each vector by combining the output voltage after Clark conversion, the instantaneous active power and the reactive power; substituting the slope into a prediction model and then calculating predicted values of the active power and the reactive power at the next moment; and selecting a switching state enabling the cost function to be minimum according to the predicted value, and acting on a grid-side converter circuit. The finite set model prediction control with the error integral provided by the invention has relatively good steady-state performance; according to the method, the optimal weight coefficient is calculated through the finite set model prediction control with the error integral, and the peak of the output power in the dynamic response process is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power electronic control, and particularly relates to a model predictive control method and system with error integration for a grid-side converter. BACKGROUND

[0002] The grid-side converter (GSC) is a core power regulation device of new energy power generation, electric traction, flexible power transmission and other systems, and undertakes tasks such as bidirectional conversion of alternating current and direct current power, grid synchronization, power factor correction and direct current bus voltage stabilization. Its performance directly affects the system efficiency, power quality and grid stability.

[0003] The traditional control method of the GSC is PI control and direct power control. The PI control has slow dynamic response and weak anti-interference ability, while the direct power control has high current harmonic content and poor steady-state performance. The finite set model predictive control (FCS-MPC) has become a research hotspot for the control of the grid-side converter due to its advantages such as no need for a modulator, flexible constraint processing and fast dynamic response.

[0004] The finite control set model predictive control (FCS-MPC) first establishes a discrete mathematical model of the system, predicts the future state in a fixed period, calculates the cost function of the controlled object and the reference object in the limited switching state, and then determines the control input acting on the inverter in the next control period through online optimization to minimize the cost function. In recent years, many advanced FCS-MPC methods have been verified in two-level grid-connected inverters and off-grid inverters.

[0005] However, in the engineering application of the grid-side converter, the prediction accuracy of the traditional FCS-MPC method is affected by parameters such as inductance and resistance, and parameter deviation can cause a large tracking error.

[0006] Disadvantages of the prior art: the prediction accuracy of the traditional FCS-MPC is greatly affected by hardware parameters, causing a large tracking error and a large peak in dynamic response. SUMMARY

[0007] Therefore, the present application aims to provide a model predictive control method and system with error integration for a grid-side converter, which can solve the existing problems.

[0008] The present application provides a finite set model predictive control (EI-MPC) with error integration. First, the integral of the power tracking error in the previous period is added to the cost function to reduce the steady-state error. Then, through reasonable weight coefficient calculation, the peak in dynamic response is avoided.

[0009] In order to achieve the above object, the application provides a model predictive control method with error integration for a grid-side converter, comprising the following steps:

[0010] According to the results of the Clark transformation of the grid voltage and the output current, the instantaneous active power and the instantaneous reactive power at the output end of the grid-side converter are calculated;

[0011] The output voltage of the space vector corresponding to different switch states in the grid-side converter is converted to the alpha-beta coordinate system through the Clark transformation;

[0012] The slope of each vector is calculated in combination with the output voltage after the Clark transformation, the instantaneous active power and the instantaneous reactive power;

[0013] The predicted values of the active power and the reactive power at the next moment are calculated after the slope is brought into a prediction model;

[0014] The switch state that minimizes the cost function is selected according to the predicted values, and is applied to the grid-side converter circuit.

[0015] In order to achieve the above object, the application further provides a model predictive control system with error integration for a grid-side converter, comprising the following steps:

[0016] An instantaneous power calculation module is configured to calculate the instantaneous active power and the instantaneous reactive power at the output end of the grid-side converter according to the results of the Clark transformation of the grid voltage and the output current;

[0017] A coordinate transformation module is configured to convert the output voltage of the space vector corresponding to different switch states in the grid-side converter to the alpha-beta coordinate system through the Clark transformation;

[0018] A slope calculation module is configured to calculate the slope of each vector in combination with the output voltage after the Clark transformation, the instantaneous active power and the instantaneous reactive power;

[0019] A prediction module is configured to calculate the predicted values of the active power and the reactive power at the next moment after the slope is brought into a prediction model;

[0020] A switch state selection module is configured to select the switch state that minimizes the cost function according to the predicted values, and is applied to the grid-side converter circuit.

[0021] In general, the advantages of the application and the experience brought to the user are as follows:

[0022] 1. The finite set model predictive control with error integration provided by the application has good steady-state performance;

[0023] 2. The finite set model predictive control with error integration provided in the application calculates the optimal weight coefficient, which avoids the output power peak in dynamic response. BRIEF DESCRIPTION OF DRAWINGS

[0024] In the drawings, like reference numerals refer to same or similar components throughout the several views. These drawings are not necessarily to scale. It should be understood that these drawings are merely schematic and certain

[0025] Figure 1 A circuit structure schematic diagram of the GSC of the application is shown.

[0026] Figure 2 An 8-switch state corresponding space vector diagram according to an embodiment of the application is shown.

[0027] Figure 3 A model predictive control method flow chart with error integration according to an embodiment of the application is shown.

[0028] Figure 4 An output power and output current waveform diagram when the active power and reactive power reference values are stepped according to an embodiment of the application is shown.

[0029] Figure 5 A configuration diagram of the model predictive control system with error integration for the grid-side converter according to an embodiment of the application is shown.

[0030] Figure 6 A structure schematic diagram of an electronic device provided by an embodiment of the application is shown.

[0031] Figure 7 A schematic diagram of a storage medium provided by an embodiment of the application is shown. DETAILED DESCRIPTION

[0032] The application will be further described below in conjunction with the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related application, and not to limit the application. In addition, it should be noted that only the parts related to the application are shown in the drawings for convenience of description.

[0033] It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict. The application will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0034] Term explanation:

[0035] Grid-Side Converter (GSC): A GSC is a power electronic converter based on fully controlled power electronic devices, primarily used to achieve efficient bidirectional conversion between AC and DC power. Its core function is to control the amplitude and phase of the output voltage through pulse width modulation technology, thereby flexibly adjusting active and reactive power. It is widely used in high-voltage DC transmission, new energy grid connection, power quality control, and other fields.

[0036] Finite Control Set Model Predictive Control (FCS-MPC): FCS-MPC first establishes a discrete mathematical model of the system. Based on the control commands and constraints, it calculates the cost function for finite switching states within a control cycle, optimizes to find the switching state that minimizes the cost function, and applies it to the next control cycle. FCS-MPC can easily handle multi-objective optimization problems; for inverters, it can calculate the cost of each switching state.

[0037] A. System Model

[0038] The GSC circuit structure with filter inductor is as follows: Figure 1 As shown, U dc The DC side voltage is represented by L and R, which are the filter inductor and resistor, respectively; i x (x = a, b, c) represents the output current; v x (x = a, b, c) represents the output voltage; e x (x = a, b, c) represent the three-phase grid voltages A, B, and C, respectively. Through different switch combinations, there are a total of 8 different switch states, and the space vectors corresponding to different switch states are as follows: Figure 2 As shown.

[0039] The mathematical model established based on the circuit structure is shown in Formula 1:

[0040]

[0041] The output current i in the three-phase stationary coordinate system x (x = a, b, c) is transformed into current i in a two-phase stationary coordinate system using Clark transformation. x (x = α, β), as shown in Formula 2:

[0042]

[0043] Output active power P g and reactive power Q g This can be expressed as Formula 3:

[0044]

[0045] Output active power P g and reactive power Q g Taking the derivative with respect to time, we get Formula 4:

[0046]

[0047] The grid voltage can be expressed as e in the two-phase stationary coordinate system, i.e., the α-β coordinate system. α and e β As shown in Formula 5:

[0048]

[0049] Where ω is the angular frequency of the grid voltage, and e is the amplitude of the three-phase grid voltage. The derivative of the grid voltage in the α-β coordinate system is shown in Equation 6:

[0050]

[0051] Formula 7 can be derived from Formula 1:

[0052]

[0053] v x (x = α, β) represents the output voltage in a two-phase stationary coordinate system. Ignoring resistance, substitute equations 6 and 7 into the formula...

[0054] Formula 8 can be obtained from Formula 4:

[0055]

[0056] B. Cost Function Design

[0057] The power error from the previous period is added to the cost function, as shown in Equation 9:

[0058]

[0059] Where m is a constant coefficient, Ts is the calculation period, and P g * Represents the power reference value for active power, Q g * indicates the power reference value for reactive power.

[0060] C. Calculate the optimal weighting coefficient m

[0061] The effect of the space vector on the active power at time k can be expressed by the slope S. P (k) represents the effect on reactive power, expressed by the slope S. Q (k) represents this, as shown in Formula 10:

[0062]

[0063] We can assume that the power reference values ​​at time k+1 and time k are equal, i.e., Equation 11:

[0064]

[0065] Combining formulas 9, 10, and 11, we obtain formula 12:

[0066]

[0067] To make the cost function value shown in Equation 12 approach 0, we can assume that both components approach 0, as shown in Equation 13:

[0068]

[0069] Combining formulas 10 and 8, we obtain formula 14:

[0070]

[0071] Combining formulas 14 and 13, we can obtain formula 15 for the z-domain:

[0072]

[0073] Multiplying both sides of Equation 1 by the conjugate of the grid voltage, we get Equation 16:

[0074]

[0075] Since the angular frequency of the grid voltage is ω, Formula 17 can be proved:

[0076]

[0077] Combining formulas 16 and 17, and separating the real and imaginary parts, we obtain formula 18:

[0078]

[0079] Combining Equations 15 and 18, the z-domain transfer function is shown in Equation 19:

[0080]

[0081] It can be seen that the proposed control method is for a second-order system. To simplify the transfer function, the value of m is designed as shown in Equation 20:

[0082]

[0083] Substituting Equation 20 into Equation 19, the transfer function can be simplified to Equation 21:

[0084]

[0085] At this point, the system is a first-order system, and the output active and reactive power can quickly reach the reference values ​​and avoid spikes.

[0086] C. Control Block Diagram

[0087] like Figure 3 As shown, the method flow of this application is as follows: First, based on the Clark transformation results of the grid voltage and output current, the instantaneous active power and reactive power at the output terminal of the grid-side converter are calculated. Then, the output voltage of the space vector corresponding to different switching states in the grid-side converter is transformed to the α-β coordinate system through Clark transformation. Next, the slope of each vector is calculated by combining the instantaneous active power and reactive power. After being substituted into the prediction model, the predicted value is calculated. Finally, the switching state that minimizes the cost function is selected and applied to the grid-side converter circuit.

[0088] To verify the effectiveness of the EI-MPC proposed in this application, a circuit was built on the Matlab / Simulink platform for simulation experiments. Experimental parameters are detailed in Table 1.

[0089] Table 1. System Parameters

[0090]

[0091] Experimental results of this application

[0092] The steady-state and dynamic performance of the proposed EI-MPC were evaluated under the following conditions: the initial reference values ​​for active and reactive power in the GSC were set to 1.2 kW and 0 kVAR, respectively. At t = 1.1 s, the active power reference value was set to 3 kW, and at t = 1.3 s, the reactive power reference value changed from 0 to 1.2 kVAR. Simulation results are as follows: Figure 4 As shown, the active and reactive power quickly tracked the power reference value, with no error or overshoot in the dynamic response. Excellent steady-state performance and good sinusoidal output current demonstrate the effectiveness of the control strategy proposed in this application.

[0093] The application provides a model predictive control system with error integral, which is used to execute the model predictive control method with error integral described in the above embodiments, such as... Figure 5 As shown, the system includes:

[0094] The instantaneous power calculation module 501 is used to calculate the instantaneous active power and reactive power at the output of the grid-side converter;

[0095] The coordinate transformation module 502 is used to transform the output voltage of the space vector corresponding to different switching states in the grid-side converter to the α-β coordinate system through Clark transformation.

[0096] The slope calculation module 503 is used to calculate the slope of each vector by combining the output voltage after Clark transformation, the instantaneous active power and reactive power;

[0097] The prediction module 504 is used to calculate the predicted values ​​of active power and reactive power at the next moment after substituting the slope into the prediction model.

[0098] The switch state selection module 505 is used to select the switch state that minimizes the cost function based on the predicted value, and applies it to the grid-side converter circuit.

[0099] The model predictive control system with error integral for grid-side converters provided in the above embodiments of this application and the model predictive control method with error integral for grid-side converters provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods used, run or implemented by their stored applications.

[0100] This application also provides an electronic device corresponding to the model predictive control method with error integral for grid-side converters provided in the foregoing embodiments, for executing the model predictive control method with error integral for grid-side converters. This application does not limit the scope of the embodiments.

[0101] Please refer to Figure 6 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 6 As shown, the electronic device 20 includes: a processor 200, a memory 201, a bus 202, and a communication interface 203. The processor 200, the communication interface 203, and the memory 201 are connected via the bus 202. The memory 201 stores a computer program that can run on the processor 200. When the processor 200 runs the computer program, it executes the model predictive control method with error integral for grid-side converters provided in any of the foregoing embodiments of this application.

[0102] The memory 201 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 203 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0103] Bus 202 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. Memory 201 is used to store programs. After receiving an execution instruction, processor 200 executes the program. The model predictive control method with error integral for grid-side converters disclosed in any of the foregoing embodiments of this application can be applied to processor 200, or implemented by processor 200.

[0104] The processor 200 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 200 or by instructions in software form. The processor 200 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf 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 may 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 modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 201. The processor 200 reads the information in memory 201 and, in conjunction with its hardware, completes the steps of the above method.

[0105] The electronic device provided in this application embodiment and the model predictive control method with error integral for grid-side converters provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they employ, operate, or implement.

[0106] This application also provides a computer-readable storage medium corresponding to the model predictive control method with error integral for grid-side converters provided in the foregoing embodiments. Please refer to [reference needed]. Figure 7 The computer-readable storage medium shown is an optical disc 30, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it executes the model predictive control method with error integral for grid-side converters provided in any of the foregoing embodiments.

[0107] It should be noted that examples of the computer-readable storage medium may also 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 optical and magnetic storage media, which will not be elaborated here.

[0108] The computer-readable storage medium provided in the above embodiments of this application and the model predictive control method with error integral for grid-side converters provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods used, run or implemented by the application programs stored therein.

[0109] It should be noted that:

[0110] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0111] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0112] Similarly, it should be understood that, in order to simplify this application and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0113] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0114] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0115] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the virtual machine creation system according to the embodiments of this application. This application can also be implemented as a device or system program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0116] It should be noted that the above embodiments are illustrative of this application and not limiting of it, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several systems, several of these systems may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0117] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A model predictive control method with error integral, used in grid-side converters, characterized in that, include: Based on the Clark transformation results of the grid voltage and output current, calculate the instantaneous active power and reactive power at the output of the grid-side converter. The output voltage of the space vector corresponding to different switching states in the grid-side converter is transformed to the α-β coordinate system using Clark transformation. The slope of each vector is calculated by combining the output voltage after Clark transformation, the instantaneous active power, and the reactive power. After substituting the slope into the prediction model, the predicted values ​​of active power and reactive power at the next moment are calculated. Based on the predicted value, the switching state that minimizes the cost function is selected and applied to the grid-side converter circuit.

2. The method according to claim 1, characterized in that, The mathematical model of the grid-side converter is as follows: Where L and R are the filter inductance and resistance of the grid-side converter, respectively; i x (x = a, b, c) represents the output current; v x (x = a, b, c) represents the output voltage; e x (x=a,b,c) represent the voltages of the three-phase power grid A, B, and C, respectively.

3. The method according to claim 1, characterized in that, In the grid-side converter, there are a total of 8 different switching states through different switch combinations, and each switching state corresponds to its own space vector.

4. The method according to claim 2, characterized in that, The active power P g and reactive power Q g Represented as: Among them, i x (x=α,β) represents the grid current in a two-phase stationary coordinate system, e x (x=α,β) represents the grid voltage in a two-phase stationary coordinate system.

5. The method according to claim 4, characterized in that, e x The calculation method for (x = α, β) is as follows: Where ω is the angular frequency of the grid voltage, e is the amplitude of the three-phase grid voltage, and t represents the time.

6. The method according to claim 4, characterized in that, The cost function is as follows: Where m is a constant coefficient, Ts is the calculation period, and P g * Represents the power reference value for active power, Q g * indicates the power reference value for reactive power, and k indicates the time.

7. The method according to claim 5, characterized in that, The slope of each vector is calculated by combining the output voltage after Clark transformation, the instantaneous active power, and the reactive power; the slope is then substituted into the prediction model to calculate the predicted values ​​of active power and reactive power for the next moment, including: The effect of the space vector on the active power at time k is expressed by the slope S. P (k) represents the effect on reactive power, expressed by the slope S. Q (k) represents the predicted values ​​of active power and reactive power at the next moment, as shown in the following formula: in, v x (x=α,β) represents the output voltage in a two-phase stationary coordinate system.

8. A model predictive control system with error integration, characterized in that, include: The instantaneous power calculation module is used to calculate the instantaneous active and reactive power at the output of the grid-side converter based on the Clark transformation results of the grid voltage and output current. The coordinate transformation module is used to transform the output voltage of the space vector corresponding to different switching states in the grid-side converter to the α-β coordinate system through Clark transformation; The slope calculation module is used to calculate the slope of each vector by combining the output voltage after Clark transformation, the instantaneous active power and reactive power; The prediction module is used to calculate the predicted values ​​of active power and reactive power at the next moment after substituting the slope into the prediction model. The switch state selection module is used to select the switch state that minimizes the cost function based on the predicted value, and applies it to the grid-side converter circuit.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by a processor to implement the method as described in any one of claims 1-7.

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