CN117856651A - Limited set sliding mode prediction control method and system for single-phase LC filter inverter - Google Patents
Limited set sliding mode prediction control method and system for single-phase LC filter inverter Download PDFInfo
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Abstract
The application provides a limited set sliding mode prediction control method and a system of a single-phase LC filter inverter. The method comprises the following steps: inputting the voltage parameter, the current parameter and nine virtual vectors of the single-phase LC filter inverter into a finite set prediction model to obtain a voltage prediction result and a current prediction result; substituting the voltage prediction result, the current prediction result and the reference value and derivative of the output voltage of the single-phase LC filter inverter into a preset sliding mode function, and outputting nine sliding mode prediction results; and obtaining the minimum value of the nine sliding mode prediction results, and using the minimum value in PWM modulation of the single-phase LC filter inverter. The application has the advantages that: easy to implement on an embedded controller; the sliding mode function is embedded into the cost function, so that the sliding mode movement of the system is realized through an optimization algorithm, and the buffeting problem can be effectively relieved; the cost function has only one parameter to be adjusted; compared with the traditional sliding mode control, the sliding mode control system has good adaptability to complex working conditions; the resonance problem of the output side filter is effectively suppressed.
Description
Technical Field
The application relates to the technical field of inverter control, in particular to a limited set sliding mode prediction control method and a system of a single-phase LC filter inverter.
Background
The distributed power generation system (distributed power generation systems, DPGS) is typically connected to the utility grid and isolated loads through voltage source inverters (voltage source inverters, VSI). As a flexible interface, VSI is expected to provide high quality uninterruptible power supply for local loads when the system is in islanding mode. It typically requires a low pass filter before connecting the load. While VSIs with LCL filters can achieve better harmonic attenuation and low current ripple, instability problems can also occur due to resonance. In contrast, a converter with LC filters can simultaneously maintain good power quality and stable operation, so such filters are a better choice in island mode power distribution.
To date, many control methods have been proposed by the industry to implement safe and efficient control of VSIs, which are largely divided into two broad categories, linear control strategies and nonlinear control strategies. In view of the nonlinear characteristics of power electronic switching devices, nonlinear controllers have proven to be more powerful in VSIs applications than linear controllers, particularly when the system is connected to nonlinear loads. Slip mode control (sliding mode control, SMC) is one of the commonly used nonlinear control schemes. It is of interest because of its fast dynamic response, high stability and strong robustness characteristics. The core idea of SMC is to force the system state to move along a predefined slip-form surface by switching the system structure. In spite of the foregoing advantages, SMC has a buffeting problem caused by system configuration switching in practical applications. Model predictive control (module predictive control, MPC) is another nonlinear control method, known for its ability to handle and optimize multi-objective problems. The performance of an MPC controller is greatly affected by the design of the cost function, which means that the corresponding parameters must be well tuned. At present, finite control set model predictive control (Finite Control Set Model Predictive Control, FCS-MPC) is a more common model predictive control strategy, and the idea is to first build a discrete mathematical model of the system, then calculate a cost function in a finite switching state in a control period according to control instructions and constraint conditions, and then determine an optimal switching state applied to the inverter at the next moment through online optimization. FCS-MPC can fully take into account the nonlinear characteristics of the system, handle complex systems with multiple discrete states, and achieve multi-objective optimization, while FCS-MPC implementation requires efficient optimization methods and computational power.
Therefore, the SMC has a buffeting problem caused by the system configuration switching in practical application. The performance of the MPC controller is limited by the design of the cost function, the parameters must be well adjusted, otherwise, it is difficult to obtain a good control effect, and in addition, the implementation of the MPC requires efficient optimization methods and calculation capabilities.
Disclosure of Invention
In view of this, the present application aims to provide a limited set sliding mode prediction control method and system for a single-phase LC filter inverter, which can solve the existing problems in a targeted manner. A limited control set predictive slipform control (finite control set predictive sliding mode control, FCS-PSMC) for single phase Voltage Source Inverters (VSIs) operating in island mode is presented. The control method is simple to realize, can be suitable for different working conditions, and ensures suppression of filter resonance while relieving system buffeting.
Based on the above objects, the present application proposes a limited set sliding mode prediction control method of a single-phase LC filter inverter, including:
inputting the voltage parameter, the current parameter and nine virtual vectors of the single-phase LC filter inverter into a finite set prediction model to obtain a voltage prediction result and a current prediction result;
substituting the voltage prediction result, the current prediction result and the reference value and derivative of the output voltage of the single-phase LC filter inverter into a preset sliding mode function, and outputting nine sliding mode prediction results;
and obtaining the minimum value of the nine sliding mode prediction results, and using the minimum value in PWM modulation of the single-phase LC filter inverter.
Further, the single-phase LC filter inverter is a HERIC topology, comprising:
the DC source, the capacitor, the HERIC inverter and the LC filter are connected in sequence;
the dc source is connected to a capacitor to stabilize the dc side voltage, and the output of the HERIC inverter is connected to an LC filter, which is further connected to a load.
Further, for the alternating-current side of the inverter, the dynamic model is obtained by applying kirchhoff voltage law and kirchhoff current law, and the specific expression is as follows:
wherein C is f ,L f Is the inductance value of the filter and the capacitance value of the filter capacitor, v o Is the output voltage of the LC filter, v inv Is the bridge arm voltage of the inverter, i L Is an inductive current, i o Is the output current.
Further, before discretizing the dynamic model, firstly creating virtual voltage vectors, and obtaining nine virtual vectors by dividing the duty ratio into four equal parts;
the dynamic model is converted into the following by Euler forward discretization:
wherein T is s Is a control period, V dc For the voltage of the direct current source, the finite set prediction model is written as:
where u represents a virtual vector.
Further, the error of the output voltage of the LC filter and its derivative are selected as state variables of the sliding mode function, and expressed as:
wherein x is 1 、x 2 Is a state variable, v ref And derivatives thereofRespectively the reference value and the derivative of the output voltage of the LC filter.
Further, the sliding mode function is defined as:
s k+1 ＝λx 1,k+1 +x 2,k+1
wherein lambda > 0 is the sliding mode coefficient of the sliding mode function, and is obtained by deduction through a pole allocation method.
Further, the value of the sliding mode function is controlled to be zero by the following cost function:
g＝|s k+1 +γ|
where γ is the offset value due to the constraints of the actual implementation.
Based on the above object, the present application further provides a limited set sliding mode prediction control system of a single-phase LC filter inverter, including:
the finite set prediction module is used for inputting the voltage parameter, the current parameter and the nine virtual vectors of the single-phase LC filter inverter into the finite set prediction model to obtain a voltage prediction result and a current prediction result;
the sliding mode function module is used for substituting the voltage prediction result, the current prediction result, the reference value of the output voltage of the single-phase LC filter inverter and the derivative thereof into a preset sliding mode function and outputting nine sliding mode prediction results;
and the PWM control module is used for solving the minimum value in the nine sliding mode prediction results and using the minimum value in PWM modulation of the single-phase LC filter inverter.
Overall, the advantages of the present application and the experience brought to the user are: the invention combines two control strategies of sliding mode control and model predictive control, integrates the advantages of the two methods, and compensates the defects of the two methods to a certain extent, and has the following advantages: 1. the proposed control method is simple to realize, has small calculation burden and is easy to realize on the embedded controller; 2. the sliding mode function is embedded into the cost function, so that the sliding mode movement of the system is realized through an optimization algorithm, and the buffeting problem can be effectively relieved; 3. the cost function is simple in design, and only one parameter needs to be adjusted and is simple to adjust; 4. compared with the traditional sliding mode control, the system has good adaptability to complex working conditions by the optimization algorithm; 5. the resonance problem of the output side filter can be effectively suppressed.
Drawings
In the drawings, the same reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily drawn to scale. It is appreciated that these drawings depict only some embodiments according to the disclosure and are not therefore to be considered limiting of its scope.
Fig. 1 shows a block diagram of a single-phase inverter system with LC filters of the present application.
Fig. 2 shows a system control block diagram according to an embodiment of the present application.
Fig. 3 shows a simulated waveform diagram of the proposed strategy. (a) a linear load having load transitions. (b) a nonlinear load. (c) With a linear load having an actual inductance and capacitance value of 60% of the model parameters.
Fig. 4 shows a Total Harmonic Distortion (THD) schematic of an output voltage according to an embodiment of the present application. (a) a linear load having load transitions. (b) a nonlinear load. (c) With a linear load having an actual inductance and capacitance value of 60% of the model parameters.
Fig. 5 shows a configuration diagram of a finite set sliding mode predictive control system of a single-phase LC filter inverter according to an embodiment of the present application.
Fig. 6 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.
Fig. 7 shows a schematic diagram of a storage medium according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Term interpretation:
slip-form control (Sliding Mode Control, SMC): is a nonlinear control strategy and aims to enable the state track of the system to quickly and stably move along a specific sliding mode surface so as to realize accurate control of the system. The core idea is to introduce a sliding mode surface, and by designing a sliding mode equation, the state track of the system moves rapidly on the sliding mode surface. The slide plane is typically selected as a hyperplane in the system state space. As the system state moves on the slip-form surface, the controller will maintain this movement and maintain this state by adjusting the system inputs. The sliding mode control has certain robustness to system parameter change and external disturbance, and can maintain the system stability to a certain extent. When the parameter design is proper, the sliding mode control can show quick and accurate control performance. However, the sliding mode control also introduces high-frequency oscillation to the system, and the buffeting problem is caused.
Finite control set model predictive control (Finite Control Set Model Predictive Control, FCS-MPC): the FCS-MPC firstly establishes a discrete mathematical model of the system, then calculates a cost function in a limited switching state in a control period according to a control instruction and constraint conditions, and then determines the optimal switching state applied to the inverter at the next moment through on-line optimization. The FCS-MPC can fully consider the nonlinear characteristics of the system, process complex systems with a plurality of discrete states and realize multi-objective optimization, and the implementation of the FCS-MPC needs an efficient optimization method and calculation capability, and meanwhile, when the multi-objective optimization is processed, a weight factor needs to be introduced, so that the complexity of the FCS-MPC design is increased.
A limited control set predictive slipform control (finite control set predictive sliding mode control, FCS-PSMC) for single phase Voltage Source Inverters (VSIs) operating in island mode is presented. The control method is simple to realize, can be suitable for different working conditions, and ensures suppression of filter resonance while relieving system buffeting. The specific implementation mode is as follows:
A. system construction
Fig. 1 is a block diagram of a single-phase inverter system with LC filters, with a common HERIC topology being taken as an example. A dc source is connected to the capacitor to stabilize the dc side voltage and the output of the HERIC inverter is connected to an LC filter which is further connected to the load.
For the alternating-current side of the inverter, the dynamic model of the system is obtained by applying kirchhoff voltage law and kirchhoff current law, and the specific expression is shown in formula (1).
Wherein C is f ,L f The filter inductance value and the filter capacitance value. v o Is the output voltage, v inv Is the bridge arm voltage of the inverter, i L Is an inductive current, i o Is the output current.
Considering the finite switching states of a single-phase current transformer, a virtual voltage vector is first created prior to discretizing the system model. By dividing the duty cycle into four equal parts, nine virtual vectors are obtained to enrich the three switching states.
The continuous linear time-invariant system model (1) is converted into (2) through Euler forward discretization.
Wherein T is s Is the control period. The predictive model may be written as:
B. control strategy
The overall control structure of FCS-PSMC proposed in the present application is shown in fig. 2, and includes the following steps:
inputting the voltage parameter, the current parameter and nine virtual vectors of the single-phase LC filter inverter into a finite set prediction model to obtain a voltage prediction result and a current prediction result;
substituting the voltage prediction result, the current prediction result and the reference value and derivative of the output voltage of the single-phase LC filter inverter into a preset sliding mode function, and outputting nine sliding mode prediction results;
and obtaining the minimum value of the nine sliding mode prediction results, and using the minimum value in PWM modulation of the single-phase LC filter inverter.
The goal of the controller is to track the voltage reference value with optimal tracking performance in the presence of uncertainty. Therefore, the error of the output voltage and its derivative are selected as state variables of the sliding mode function, and are discretized to be represented as (4).
Wherein x is 1 、x 2 And is a state variable, v ref And derivatives thereofRespectively the reference value of the output voltage and its derivative. The sliding mode function may be defined as:
s k+1 ＝λx 1,k+1 +x 2,k+1 (5)
wherein lambda > 0 is the sliding mode coefficient of the sliding mode function, and can be obtained by deduction through a pole allocation method.
The core of the SMC is to drive a state variable to move near the slip-form surface. By combining with the online rolling optimization idea of the MPC, a cost function is designed to control the value of a system sliding mode function to be zero.
g＝|s k+1 +γ| (6)
Where γ is the offset value due to the constraints of the actual implementation. For example, considering over-current protection, when the predicted current exceeds the maximum output current, γ will be assigned to infinity (or a sufficiently large number compared to other vectors) to avoid over-current failure of the system.
Compared with the traditional sliding mode control, the method provided by the application can perform optimal evaluation on the cost function by enumerating nine virtual vectors, so that jitter is eliminated, and particularly when interference from parameter mismatch and system uncertainty cannot be ignored. The offset term gamma indicates that the mechanism of online rolling optimization confers adaptability to complex operating conditions to the algorithm.
From the perspective of the conventional MPC, it is noted that the FCS-PSMC strategy proposed in this application includes the derivative of the control target in the cost function, which is related to the inverter output current, since the inverter side current is used for feedback control, the resonance of the LC filter can be well suppressed.
To verify the control strategy proposed in the present application, the present application was simulated under three different operating conditions. As shown in fig. 3 and 4, the results show that FCS-PSMC exhibits high stability when connecting nonlinear loads and strong robustness in the event of parameter mismatch.
The application embodiment provides a limited set sliding mode prediction control system of a single-phase LC filter inverter, which is used for executing the limited set sliding mode prediction control method of the single-phase LC filter inverter described in the above embodiment, as shown in fig. 5, and the system includes:
the finite set prediction module 501 is configured to input a voltage parameter, a current parameter and nine virtual vectors of the single-phase LC filter inverter into a finite set prediction model to obtain a voltage prediction result and a current prediction result;
the sliding mode function module 502 is configured to substitute the voltage prediction result, the current prediction result, the reference value of the output voltage of the single-phase LC filter inverter and the derivative thereof into a preset sliding mode function, and output nine sliding mode prediction results;
the PWM control module 503 is configured to find a minimum value of the nine sliding-mode prediction results, and use the minimum value in PWM modulation of the single-phase LC filter inverter.
The limited-set sliding-mode prediction control system of the single-phase LC filter inverter provided by the above embodiment of the present application and the limited-set sliding-mode prediction control method of the single-phase LC filter inverter provided by the embodiment of the present application are the same inventive concept, and have the same beneficial effects as the method adopted, operated or implemented by the stored application program thereof.
The embodiment of the application also provides an electronic device corresponding to the limited set sliding mode prediction control method of the single-phase LC filter inverter provided by the previous embodiment, so as to execute the limited set sliding mode prediction control method of the upper single-phase LC filter inverter. The embodiments of the present application are not limited.
Referring to fig. 6, a schematic diagram of an electronic device according to some embodiments of the present application is shown. As shown in fig. 6, 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 being connected by the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the limited set sliding mode prediction control method of the single-phase LC filter inverter provided in any of the foregoing embodiments of the present application when executing the computer program.
The memory 201 may include a high-speed random access memory (RAM: random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the system network element and at least one other network element is implemented via at least one communication interface 203 (which may be wired or wireless), the internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
Bus 202 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. The memory 201 is configured to store a program, and the processor 200 executes the program after receiving an execution instruction, and the limited set sliding mode prediction control method of the single-phase LC filter inverter disclosed in any embodiment of the present application may be applied to the processor 200 or implemented by the processor 200.
The processor 200 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 200 or by instructions in the form of software. The processor 200 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present application may be embodied directly in hardware, in a decoded processor, or in a combination of hardware and software modules in a decoded processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201, and in combination with its hardware, performs the steps of the above method.
The electronic equipment provided by the embodiment of the application and the limited set sliding mode prediction control method of the single-phase LC filter inverter provided by the embodiment of the application have the same beneficial effects as the method adopted, operated or realized by the electronic equipment and the limited set sliding mode prediction control method of the single-phase LC filter inverter due to the same inventive concept.
The present embodiment also provides a computer readable storage medium corresponding to the limited set sliding mode prediction control method of the single-phase LC filter inverter provided in the foregoing embodiment, referring to fig. 7, the computer readable storage medium is shown as an optical disc 30, and a computer program (i.e. a program product) is stored thereon, where the computer program, when executed by a processor, performs the limited set sliding mode prediction control method of the single-phase LC filter inverter provided in any of the foregoing embodiments.
It should be noted that examples of the computer readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical or magnetic storage medium, which will not be described in detail herein.
The computer readable storage medium provided by the above embodiment of the present application and the limited set sliding mode prediction control method of the single-phase LC filter inverter provided by the embodiment of the present application have the same beneficial effects as the method adopted, operated or implemented by the application program stored therein, because of the same inventive concept.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, the present application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and the above description of specific languages is provided for disclosure of preferred embodiments of the present application.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed application requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the present application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functions of some or all of the components in a virtual machine creation system according to embodiments of the present application may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present application may also be embodied as a device or system program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present application may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of various changes or substitutions within the technical scope of the present application, and these should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A limited set sliding mode prediction control method of a single-phase LC filter inverter is characterized by comprising the following steps:
inputting the voltage parameter, the current parameter and nine virtual vectors of the single-phase LC filter inverter into a finite set prediction model to obtain a voltage prediction result and a current prediction result;
substituting the voltage prediction result, the current prediction result and the reference value and derivative of the output voltage of the single-phase LC filter inverter into a preset sliding mode function, and outputting nine sliding mode prediction results;
and obtaining the minimum value of the nine sliding mode prediction results, and using the minimum value in PWM modulation of the single-phase LC filter inverter.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the single-phase LC filter inverter is a HERIC topology comprising:
the DC source, the capacitor, the HERIC inverter and the LC filter are connected in sequence;
the dc source is connected to a capacitor to stabilize the dc side voltage, and the output of the HERIC inverter is connected to an LC filter, which is further connected to a load.
3. The method as recited in claim 2, further comprising:
for the alternating-current side of the inverter, the dynamic model is obtained by applying kirchhoff voltage law and kirchhoff current law, and the specific expression is as follows:
wherein C is f ,L f Is the inductance value of the filter and the capacitance value of the filter capacitor, v o Is the output voltage of the LC filter, v inv Is the bridge arm voltage of the inverter, i L Is an inductive current, i o Is the output current.
4. A method as claimed in claim 3, further comprising:
before discretizing the dynamic model, firstly creating virtual voltage vectors, and obtaining nine virtual vectors by dividing the duty ratio into four equal parts;
the dynamic model is converted into the following by Euler forward discretization:
wherein T is s Is a control period, V dc For the voltage of the direct current source, the finite set prediction model is written as:
where u represents a virtual vector.
5. The method as recited in claim 4, further comprising:
the error of the output voltage of the LC filter and the derivative thereof are selected as state variables of a sliding mode function, and the state variables are expressed as follows after discretization:
wherein x is 1 、x 2 Is a state variable, v ref And derivatives thereofRespectively the reference value and the derivative of the output voltage of the LC filter.
6. The method as recited in claim 5, further comprising:
the sliding mode function is defined as:
s k+1 ＝λx 1,k+1 +x 2,k+1
wherein lambda > 0 is the sliding mode coefficient of the sliding mode function, and is obtained by deduction through a pole allocation method.
7. The method as recited in claim 6, further comprising:
controlling the value of the sliding mode function to be zero by the following cost function:
g＝|s k+1 +γ|
where γ is the offset value due to the constraints of the actual implementation.
8. A limited set sliding mode predictive control system for a single phase LC filter inverter, comprising:
the finite set prediction module is used for inputting the voltage parameter, the current parameter and the nine virtual vectors of the single-phase LC filter inverter into the finite set prediction model to obtain a voltage prediction result and a current prediction result;
the sliding mode function module is used for substituting the voltage prediction result, the current prediction result, the reference value of the output voltage of the single-phase LC filter inverter and the derivative thereof into a preset sliding mode function and outputting nine sliding mode prediction results;
and the PWM control module is used for solving the minimum value in the nine sliding mode prediction results and using the minimum value in PWM modulation of the single-phase LC filter inverter.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor runs the computer program to implement the method of any one of claims 1-7.
10. A computer readable storage medium having stored thereon a computer program, wherein the program is executed by a processor to implement the method of any of claims 1-7.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190288611A1 (en) * | 2018-03-13 | 2019-09-19 | Shanghai Jiao Tong University | Nonlinear control method for micro-grid inverter with anti-disturbance |
| CN114597955A (en) * | 2022-04-11 | 2022-06-07 | 青岛理工大学 | Three-phase LCL grid-connected NPC inversion system based on rapid model predictive control |
| CN115995846A (en) * | 2023-02-03 | 2023-04-21 | 安徽大学 | Model-free predictive control method and equipment for LC filtering type voltage source inverter |
| CN116436273A (en) * | 2023-03-14 | 2023-07-14 | 苏州大学 | Control method and system for three-phase three-level inverter |
| CN116599367A (en) * | 2023-05-19 | 2023-08-15 | 燕山大学 | Passive integral terminal sliding mode prediction control method based on current source type converter |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190288611A1 (en) * | 2018-03-13 | 2019-09-19 | Shanghai Jiao Tong University | Nonlinear control method for micro-grid inverter with anti-disturbance |
| CN114597955A (en) * | 2022-04-11 | 2022-06-07 | 青岛理工大学 | Three-phase LCL grid-connected NPC inversion system based on rapid model predictive control |
| CN115995846A (en) * | 2023-02-03 | 2023-04-21 | 安徽大学 | Model-free predictive control method and equipment for LC filtering type voltage source inverter |
| CN116436273A (en) * | 2023-03-14 | 2023-07-14 | 苏州大学 | Control method and system for three-phase three-level inverter |
| CN116599367A (en) * | 2023-05-19 | 2023-08-15 | 燕山大学 | Passive integral terminal sliding mode prediction control method based on current source type converter |
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