Inverter single-vector finite control set MPC optimization method and device based on suboptimal switch state switching

Abstract

The present invention discloses an inverter single-vector finite control set MPC optimization method and device based on suboptimal switch state switching. First, the three-phase current on the filter side and the current and voltage on the output side are collected, the output voltage at the next sampling moment is predicted, the switch state corresponding to the minimum cost function is selected as the optimal switch state, and the switch state corresponding to the second minimum cost function is recorded as the suboptimal switch state. Then, it is determined whether the optimal switch state is the same as the current switch state. If they are different, the optimal switch state is executed; if they are the same, the suboptimal switch state is switched. Finally, the suboptimal switch state switching time is calculated, and the suboptimal switch state is switched to at the switching time to complete the control action. The present invention effectively improves the power quality of the inverter output voltage by selecting the suboptimal switch state switching. At the same time, the calculation amount of the suboptimal switching time in the present invention is small, the implementation is simple, and there is no need to increase the sampling input and calculate the new prediction value.

Description

Technical Field

[0001] The present invention relates to the field of power electronics technology, and in particular to an inverter single-vector finite control set (MPC) optimization method and device based on suboptimal switch state switching. Background Art

[0002] Since the 1980s, model predictive control (MPC) has found numerous applications in industrial process control. Due to its advantages, such as simple modeling, easy parameter tuning, and strong robustness, it has been gradually extended to fields such as power electronics control and power system scheduling in recent years. In the power electronics field, inverters are commonly controlled, characterized by a limited number of discrete switching states. This makes them suitable for finite control set MPC (FCS-MPC), where all switching states are substituted into the inverter predictive model. The switching state with the lowest cost function is considered the optimal switching state, and the switching action is executed. FCS-MPC is currently the most widely used inverter MPC method because it does not require PWM modulation and is relatively computationally intensive.

[0003] FCS-MPC traverses all possible switching states of the inverter through a prediction model, calculates a cost function, selects the switching state with the minimum cost function as the optimal switching state, and switches to the newly selected switching state when the inverter sampling time arrives. Because multiple consecutive switching states may be selected for the same switching, the switching states are identical. When the inverter switch remains on and off for a relatively long time, only using a single switching state within the same sampling period not only reduces the life of the switching device but also degrades the power quality of the output voltage, resulting in high total harmonic distortion and unstable switching frequency. Therefore, improving the power quality of FCS-MPC is a research hotspot in the field of power electronics control. Summary of the Invention

[0004] In response to the above problems, the present invention proposes an inverter single-vector finite control set (MPC) optimization method and device based on suboptimal switch state switching, which is used for controlling the output voltage of a voltage source three-phase inverter. The present invention not only stabilizes the switching frequency, but also maximizes the utilization of each control cycle, thereby improving the power quality of the inverter output voltage.

[0005] To achieve the above object, the present invention provides an inverter single-vector finite control set (MPC) optimization method based on suboptimal switch state switching, comprising the following steps:

[0006] Step 1: Obtain the filter current and output side current and voltage, and establish the inverter discretization prediction model;

[0007] Step 2: Traverse all inverter switching states and obtain the optimal switching state and suboptimal switching state based on the inverter discretization prediction model;

[0008] Step 3: If the current switching state is a suboptimal switching state, the optimal switching state is switched immediately; if the current switching state is an optimal switching state, the suboptimal switching state is switched and the suboptimal switching state is selected as the switching state of the inverter in the next cycle;

[0009] Step 4: Calculate the suboptimal switching time T based on the predicted output voltage and the reference voltage com ;

[0010] Step 5: At the suboptimal switching time T com Switch to the suboptimal switching state to complete the control action.

[0011] Furthermore, in step 1, the three-phase current on the filter side and the output side current and voltage are collected and transformed into a two-phase stationary alpha-beta coordinate system to obtain the filter current and the output side current and voltage respectively, and a discrete prediction model of the inverter in the alpha-beta coordinate system is established, and the predicted output voltage is calculated.

[0012] Furthermore, in step 2, the cost function of each switching state is calculated based on the inverter discretization prediction model, the switching state corresponding to the minimum cost function is selected as the optimal switching state, and the switching state corresponding to the second smallest cost function is recorded as the suboptimal switching state.

[0013] Furthermore, in step 4, the suboptimal switching time is calculated based on the predicted output voltage corresponding to the suboptimal switching state and the cost function, the predicted output voltage corresponding to the optimal switching state and the cost function, and the reference voltage.

[0014] Furthermore, the suboptimal switching time T is calculated in step 4. com The specific process is:

[0015] If the current switch state is the optimal switch state, the suboptimal switch state switching time T com for:

[0016]

[0017] Among them, µ is the combination coefficient, Indicates the sampling period.

[0018] Furthermore, after the suboptimal switching time T com The predicted output voltage is compared with the reference output voltage, and the square of the spatial distance between the two voltage vectors is obtained by decomposing it on the alpha axis and the beta axis, and then the combination coefficient when the distance is minimum is calculated.

[0019] Furthermore, the combination coefficient µ is calculated as follows:

[0020]

[0021] Among them, the optimal switching state n opt The predicted output voltage v o opt The components of (k+1) on the alpha and beta axes are denoted as v oα opt (k+1) and v oβ opt (k+1), suboptimal switching state n sub The predicted output voltage v o sub The components of (k+1) on the alpha and beta axes are denoted as v oα sub (k+1) and v oβ sub (k+1), g subα and g subβ are the components of the cost function of the suboptimal switching state on the alpha and beta axes, respectively.

[0022] On the other hand, the present invention provides an inverter single-vector finite control set MPC optimization device based on suboptimal switch state switching, the device comprising an inverter discretization prediction model construction module, a switch state judgment module, a suboptimal switching time calculation module and a switch state switching module;

[0023] The inverter discretization prediction model building module is used to obtain the filter current and the output side current and voltage, and establish the inverter discretization prediction model;

[0024] The switch state judgment module is used to traverse all inverter switch states and obtain the optimal switch state and the suboptimal switch state based on the inverter discretization prediction model; if the current switch state is the suboptimal switch state, the optimal switch state is immediately switched; if the current switch state is the optimal switch state, the suboptimal switch state is switched and the suboptimal switch state is selected as the switch state of the inverter in the next cycle;

[0025] The suboptimal switching time calculation module is used to calculate the suboptimal switching time T based on the predicted output voltage and the reference voltage. com ;

[0026] The switch state switching module is used to switch at a switching time T com Switch to the suboptimal switching state to complete the control action.

[0027] Beneficial effects of the present invention:

[0028] (1) Compared with the traditional single-vector finite control set MPC that only uses the optimal switch state switching, the present invention maximizes the control efficiency per cycle while maintaining a fixed switching frequency by adding the option of suboptimal switch state switching, and effectively improves the power quality of the inverter output voltage by dynamically adjusting the switching duration.

[0029] (2) The calculation amount of the suboptimal switching time in the present invention is small and simple to implement, and there is no need to increase the sampling input and calculate the new prediction value. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 The flow chart and inverter connection model of LC inverter are implemented for the suboptimal switching optimization method based on the inverter single vector finite control set MPC.

[0031] Figure 2 This is an analysis diagram of the inverter output voltage under the traditional finite control set MPC.

[0032] Figure 3 This is an analysis diagram of the inverter output voltage under the control of the present invention.

[0033] Figure 4 The figure is a schematic diagram of a suboptimal switching optimization device based on an inverter single vector finite control set MPC according to the present invention. DETAILED DESCRIPTION

[0034] The specific embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0035] like Figure 1 As shown, the present invention provides an inverter single-vector finite control set MPC optimization method based on suboptimal switch state switching, and the specific implementation scheme is as follows:

[0036] Step 1: Collect Figure 1 The three-phase current on the filter side and the output side current and voltage when the LC inverter is connected to the AC bus model are shown. These currents are transformed into a two-phase stationary alpha-beta coordinate system to obtain the filter current and output side current and voltage, respectively. The discretized prediction model of the inverter in the alpha-beta coordinate system is established:

[0037]

[0038]

[0039]

[0040] Where Ts represents the sampling period, k represents the current sampling time, k+1 represents the next sampling time, and if (k) is the filter current at the current sampling moment, v o (k) is the output voltage at the current sampling moment, i o (k) is the output current at the current sampling moment, v o (k+1), i o (k+1) are the predicted output voltage and current at the next sampling moment, v n (k) is the inverter output voltage vector, n ranges from 0 to 7, corresponding to 8 groups of inverter switch states, g n represents the cost function of model predictive control MPC under different switching states, v oα (k+1), v oβ (k+1) are v o (k+1) components on the alpha and beta axes, v α * (k+1), v β * (k+1) is the inverter reference output voltage v * (k+1) are the components on the alpha and beta axes respectively, Cf is the capacitance value of the LC filter, and Lf is the inductance value of the LC filter.

[0041] Step 2: According to the inverter discretization prediction model in step 1, traverse the 8 switching states of the inverter and select the switching state corresponding to the minimum cost function as the optimal switching state, which is recorded as n opt , record the switch state corresponding to the second smallest cost function as the suboptimal switch state n sub , determine the optimal switching state n opt Is it the same as the current switch state n? If different, execute the optimal switch state n opt ; If they are the same, execute the suboptimal switch state n sub Switching means selecting the suboptimal switching state as the switching state of the inverter in the next cycle.

[0042] Step 3: Optimal switching state n opt The predicted output voltage v o opt The components of (k+1) on the alpha and beta axes are denoted as v oα opt (k+1) and v oβ opt (k+1), suboptimal switching state n sub The predicted output voltage v o sub The components of (k+1) on the alpha and beta axes are denoted as v oα sub (k+1) and voβ sub (k+1), the components of the reference output voltage v*(k+1) on the alpha axis and beta axis are recorded as v α *(k+1),v β *(k+1); when the current switch state is the same as the optimal switch state, the suboptimal switching time T com for:

[0043]

[0044] in,

[0045]

[0046] Among them, µ is the combination coefficient, g subα and g subβ are the components of the cost function of the suboptimal switching state on the alpha and beta axes, respectively.

[0047] Furthermore, the determination of the delayed switching time in step 3 is further explained:

[0048] When the optimal switching state n opt When the current switch state n is the same, after the suboptimal switching time T com The predicted output voltage v o com (k+1) is expressed as:

[0049]

[0050] At this time, v obtained by combining the combination coefficient µ o com The error between (k+1) and v*(k+1) is the smallest, and the corresponding suboptimal switching time is µTs.

[0051] Step 4: At the switching time T com Switch to the suboptimal switching state to complete the control action.

[0052] In order to demonstrate the control effect of the present invention, the inverter using the traditional FCS-MPC is compared with the inverter using the control method of the present invention. The sampling frequency is 10k, the reference output voltage amplitude is 311V, and the frequency is 50Hz. Figure 2 The figure shows the output voltage of the inverter under the traditional finite control set MPC. The FFT fast Fourier transform analysis function of MATLAB / SIMULINK is used to analyze the output voltage of the inverter phase a in the steady state, and its amplitude is 308.8V and the total harmonic distortion rate is 1.23%. Figure 3The figure shows the inverter output voltage under the control of the present invention. Using the same spectrum analysis function to analyze the inverter's phase A output voltage in steady state, the amplitude is 310.3V and the total harmonic distortion is 1.07%. This shows that the inverter controlled by the present invention achieves an output voltage amplitude closer to the reference value and a lower total harmonic distortion, improving the power quality of the inverter output voltage.

[0053] Corresponding to the aforementioned embodiment of an inverter single-vector finite control set MPC optimization method based on suboptimal switch state switching, the present invention also provides an embodiment of an inverter single-vector finite control set MPC optimization device based on suboptimal switch state switching.

[0054] like Figure 4 As shown, the present invention also provides an inverter single-vector finite control set MPC optimization device based on suboptimal switch state switching, which includes an inverter discretization prediction model construction module, a switch state judgment module, a suboptimal switching time calculation module and a switch state switching module;

[0055] The inverter discretization prediction model construction module is used to collect the three-phase current on the filter side and the output side current and voltage, and transform them into a two-phase static alpha-beta coordinate system to obtain the filter side current and the output side current and voltage respectively, establish the inverter discretization prediction model in the alpha-beta coordinate system, and calculate the predicted output voltage;

[0056] The switch state judgment module is used to traverse the switch state of the inverter. The switch state judgment module is used to traverse the switch state of the inverter. According to the calculated cost function, the switch state corresponding to the minimum cost function is selected as the optimal switch state, and the switch state corresponding to the second minimum cost function is recorded as the suboptimal switch state. It is judged whether the optimal switch state is the same as the current switch state. If different, the optimal switch state is executed; if the same, the suboptimal switch state is switched, that is, the suboptimal switch state is selected as the switch state of the inverter in the next cycle.

[0057] The suboptimal switching time calculation module is used to calculate the suboptimal switching time T according to the predicted output voltage corresponding to the suboptimal switching state and the cost function, the predicted output voltage corresponding to the optimal switching state and the cost function and the reference voltage. com ;

[0058] The switch state switching module is used to switch at a switching time T com Switch to the suboptimal switching state to complete the control action.

[0059] The implementation process of the functions and effects of each module in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0060] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, and the modules described as separate components may or may not be physically separate. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present invention. Those skilled in the art can understand and implement them without expending creative work.

[0061] The above embodiments are used to illustrate the present invention rather than to limit the present invention. Any modifications and changes made to the present invention within the spirit of the present invention and the protection scope of the claims shall fall within the protection scope of the present invention.

Claims

1. An inverter single-vector finite control set MPC optimization method based on suboptimal switch state switching, characterized in that: The steps include: Step 1: Obtain the filter current and output side current and voltage, and establish the inverter discretization prediction model; Step 2: Traverse all inverter switching states and obtain the optimal switching state and suboptimal switching state based on the inverter discretization prediction model; Step 3: If the current switching state is a suboptimal switching state, the optimal switching state is switched immediately; if the current switching state is an optimal switching state, the suboptimal switching state is switched and the suboptimal switching state is selected as the switching state of the inverter in the next cycle; Step 4: Calculate the suboptimal switching time T based on the predicted output voltage and the reference voltage com Specifically: the suboptimal switching time is calculated based on the predicted output voltage and cost function corresponding to the suboptimal switching state, the predicted output voltage and cost function corresponding to the optimal switching state, and the reference voltage. If the current switching state is the optimal switching state, the suboptimal switching state switching time T com for: ； Among them, µ is the combination coefficient, Represents the sampling period; after the suboptimal switching time T com The predicted output voltage is compared with the reference output voltage, and the square of the spatial distance between the two voltage vectors is obtained by decomposing it on the alpha axis and the beta axis, and then the combination coefficient when the distance is minimum is calculated; Step 5: At the suboptimal switching time T com Switch to the suboptimal switching state to complete the control action.

2. The inverter single vector finite control set (MPC) optimization method based on suboptimal switch state switching according to claim 1, characterized in that: In step 1, the three-phase current on the filter side and the output side current and voltage are collected and transformed into a two-phase stationary alpha-beta coordinate system to obtain the filter current and output side current and voltage respectively. A discrete prediction model of the inverter in the alpha-beta coordinate system is established, and the predicted output voltage is calculated.

3. The inverter single-vector finite control set (MPC) optimization method based on suboptimal switch state switching according to claim 1, characterized in that: In step 2, the cost function of each switching state is calculated based on the inverter discretization prediction model, the switching state corresponding to the minimum cost function is selected as the optimal switching state, and the switching state corresponding to the second smallest cost function is recorded as the suboptimal switching state.

4. The inverter single vector finite control set (MPC) optimization method based on suboptimal switch state switching according to claim 1, characterized in that: The combination coefficient µ is calculated as follows: ； ； Among them, the optimal switching state n opt The predicted output voltage v o opt The components of (k+1) on the alpha and beta axes are denoted as v oα opt (k+1) and v oβ opt (k+1), suboptimal switching state n sub The predicted output voltage v o sub The components of (k+1) on the alpha and beta axes are denoted as v oα sub (k+1) and v oβ sub (k+1), g subα and g subβ are the components of the cost function of the suboptimal switching state on the alpha axis and the beta axis respectively; g n represents the cost function of model predictive control MPC under different switching states, (k+1), (k+1) are v o (k+1) components on the alpha and beta axes, (k+1), (k+1) is the inverter reference output voltage v * (k+1) components on the alpha and beta axes respectively.

5. An inverter single-vector finite control set (MPC) optimization device based on suboptimal switch state switching for implementing the method according to any one of claims 1 to 4, characterized in that: The device includes an inverter discretization prediction model building module, a switch state judgment module, a suboptimal switching time calculation module and a switch state switching module; The inverter discretization prediction model building module is used to obtain the filter current and the output side current and voltage, and establish the inverter discretization prediction model; The switch state judgment module is used to traverse all inverter switch states and obtain the optimal switch state and the suboptimal switch state based on the inverter discretization prediction model; if the current switch state is the suboptimal switch state, the optimal switch state is immediately switched; if the current switch state is the optimal switch state, the suboptimal switch state is switched and the suboptimal switch state is selected as the switch state of the inverter in the next cycle; The suboptimal switching time calculation module is used to calculate the suboptimal switching time T based on the predicted output voltage and the reference voltage com ; The switch state switching module is used to switch at a switching time T com Switch to the suboptimal switching state to complete the control action.

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