A fractional-order finite-set control method based on a single-phase quasi-Z-source rectifier

Abstract

This invention discloses a fractional-order finite set model control method based on a single-phase quasi-Z-source rectifier, belonging to the field of power electronics. Compared with traditional integer-order finite set model predictive control and fractional-order finite set model predictive control without delay compensation, it reduces the input-side AC current distortion rate, decreases the output-side DC voltage fluctuation, improves the dynamic response speed of the output-side DC voltage, and has higher control accuracy.

Description

Technical Field

[0001] This invention relates to the field of power electronics technology, and in particular to a fractional-order finite control set model control method based on a single-phase quasi-Z source rectifier. Background Technology

[0002] With the development of power electronics technology, PWM rectifiers have been widely used in power systems, electric drives, and other fields. Among them, quasi-Z-source rectifiers have broad application prospects due to their simple topology, ability to boost or buck voltage, allow shoot-through, and high reliability, and are currently used in on-board integrated chargers. Traditional quasi-Z-source rectifiers use a dual closed-loop control system, with both the inner current loop and outer voltage loop employing PI control, which suffers from slow dynamic response and complex control parameter tuning. To overcome these shortcomings, model predictive control has been widely used in PWM rectifiers. Model predictive control features good dynamic performance, simple design, and the ability to achieve multi-objective control. However, model predictive control still has shortcomings in terms of control accuracy.

[0003] With the development of fractional calculus theory, fractional modeling and control have come into focus. Compared with traditional integer-order modeling, fractional calculus equations can better describe the actual operating state of rectifiers. Compared with traditional control, fractional control adds the adjustable parameter of fractional order, resulting in more flexible control and higher control accuracy. Introducing fractional calculus operators into traditional model predictive control yields fractional model predictive control. However, considering that sampling and computation both require time, traditional model predictive control still suffers from a one-cycle control delay, affecting the algorithm's control accuracy. Summary of the Invention

[0004] This invention provides a fractional-order finite control set model control method based on a single-phase quasi-Z source rectifier, which can achieve the control objectives of rectifier operation at unity power factor, reduced distortion rate of AC input current, improved power quality, smaller output DC voltage fluctuation, and improved steady-state and dynamic performance.

[0005] This invention provides a fractional-order finite control set model control method based on a single-phase quasi-Z-source rectifier, comprising the following steps:

[0006] The AC input current, DC quasi-Z source network inductor current, and capacitor voltage are selected as control targets. The operating characteristics of the single-phase quasi-Z source rectifier are analyzed. Based on the analysis of the AC side circuit, the state equations corresponding to the AC input current under different states of the rectifier bridge switch are obtained. Fractional calculus operators are introduced, and fractional state equations are used to replace integer state equations. Based on the modal analysis of the DC equivalent circuit, the state equations of the quasi-Z source network inductor current and capacitor voltage under different circuit states are obtained. When the circuit is in a non-shoo-through state, the switch is turned on; when the circuit is in a shoot-through state, the switch is turned off.

[0007] Using the forward Euler method, the state equations of the selected inductor current and capacitor voltage are discretized to obtain the prediction models of the required current and voltage. The possible combinations of switching states of the rectifier bridge switch and the quasi-Z source network switch are listed. The combinations are mapped to the prediction models to construct the value function of the associated multi-objective control and to compensate for the delay time.

[0008] The required voltage and current are sampled by voltage and current sensors. Based on the sampled and calculated values, predictive control is performed using an improved fractional-order finite control set model. Through traversal optimization, the switching state combination that minimizes the value function is obtained and determined as the optimal switching state combination. Based on the optimal switching state combination, the switching states of the rectifier bridge switch and the switches in the quasi-Z source network are determined.

[0009] Optionally, in one embodiment of the present invention, the AC side circuit of the single-phase quasi-Z source rectifier is analyzed, a fractional-order calculus operator is introduced to obtain the state equation of the input AC current, and the fractional-order state equation is discretized using the forward Euler method to obtain the fractional-order prediction expression of the input AC current as follows:

[0010]

[0011] Among them, i g (k+1) and i g (k) represents the AC input current value at sampling times k+1 and k, respectively; T s For control cycle; L g This refers to the AC side inductance value; u g (k) and u pn (k) represents the AC input voltage and rectifier bridge output voltage at the k-th sampling time, respectively; α is the order of the introduced fractional calculus operator; S1 is the rectifier's operating mode, with a value of -1, 0, or 1;

[0012] The DC-side circuit of a single-phase quasi-Z-source rectifier is analyzed to obtain the state equations for the capacitor voltage and inductor current of the quasi-Z-source network. The state equations are then discretized using the forward Euler method, yielding the predicted expression for the inductor current:

[0013]

[0014] Among them, i L2 (k+1) and i L2 (k) represents the inductor current value of the quasi-Z source network at sampling times k+1 and k, respectively; u C2 (k), u C1 (k) and u Co (k) represents the quasi-Z source network capacitor voltage and output DC voltage at the k-th sampling time, respectively; S2 is the switching state of the quasi-Z source network switch, which takes the value of 0 or 1. When the rectifier is in the shoot-through state, S2 takes the value of 0, and when the rectifier is in the non-shoot-through state, S2 takes the value of 1.

[0015] The predicted expression for the capacitor voltage is:

[0016]

[0017] Among them, u C2 (k+1) and u C2 (k) represents the capacitor voltage values ​​of the quasi-Z source network at sampling times k+1 and k, respectively; i pn (k), i L2 (k) and i L1 (k) represents the output current value of the rectifier bridge and the inductor current value of the quasi-Z source network at the k-th sampling time, respectively.

[0018] Optionally, in one embodiment of the present invention, the value function for constructing the associated multi-objective control is:

[0019]

[0020] Among them, i g (k+1),i L2 (k+1) and u C2 (k+1) represent the AC input current value and the inductor current and capacitor voltage values ​​of the quasi-Z source network at the (k+1)th sampling time, respectively; λ is the weighting factor; i gref 、i L2ref and u C2ref These represent the reference values ​​for the AC input current, the quasi-Z source network inductor current, and the capacitor voltage, respectively; i gref The voltage is obtained by multiplying the output of the outer loop of the quasi-Z source network capacitor voltage controlled by PI with the phase of the AC side input voltage, to achieve unity power factor operation; L2ref It is obtained from the output of the outer loop of the DC output voltage controlled by a PI controller; u C2ref It is a fixed constant.

[0021] Optionally, in one embodiment of the present invention, compensation for the delay time includes:

[0022] A two-step prediction method is adopted to compensate for control delay by combining one-step prediction with fractional-order model prediction. That is, the optimal switching state at this moment is obtained in the previous cycle.

[0023] The fractional-order finite-set model control method for a single-phase quasi-Z-source rectifier, as described in this invention, can achieve the control objective of stable DC output voltage while maintaining the rectifier's power factor. Compared with traditional integer-order model predictive control and model predictive control without delay compensation strategies, the control performance disclosed in this invention is more flexible and has higher control accuracy. Under the same conditions, this invention reduces the distortion rate of the AC input current of the single-phase quasi-Z-source rectifier, improves power quality, makes the output DC voltage more stable, reduces voltage fluctuations, and enhances the rectifier's dynamic performance and robustness. When the system receives external interference, the rectifier can quickly adjust to achieve stable output DC voltage.

[0024] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0025] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0026] Figure 1 This is a schematic diagram of the topology of a single-phase quasi-Z source rectifier in an embodiment of the present invention;

[0027] Figure 2 This is a control block diagram of a single-phase quasi-Z source rectifier in an embodiment of the present invention;

[0028] Figure 3 This is a flowchart of the main steps of the improved fractional-order finite control set model predictive control method for a single-phase quasi-Z source rectifier in this embodiment of the invention.

[0029] Figure 4 This is a simplified diagram illustrating the principle of the fractional-order finite control set model predictive control delay compensation strategy in this embodiment of the invention.

[0030] Figure 5 (a) and Figure 5 (b) are comparison diagrams of the voltage and current simulation waveforms of the single-phase quasi-Z source rectifier before and after the addition of the delay compensation strategy in the embodiments of the present invention. Figure 5 (c) and Figure 5(d) are comparison charts of the percentage of current harmonic content (THD) of the AC side current of the single-phase quasi-Z source rectifier before and after the addition of the delay compensation strategy in the embodiments of the present invention.

[0031] Figure 6 This is a comparison chart of the percentage of current harmonic content (THD) of the AC side current of a single-phase quasi-Z source rectifier to the fundamental current content under different input voltages and output powers, under the improved fractional-order finite control set model predictive control, integer-order finite control set model predictive control, and fractional-order finite control set model predictive control in the embodiments of the present invention. (a) represents the comparison chart of THD under the three control methods when the input voltage is different, and (b) represents the comparison chart of THD under the three control methods when the output power is different.

[0032] Figure 7 The above are simulation waveform comparison diagrams of the output voltage change of a single-phase quasi-Z source rectifier when the given output voltage changes, under the improved fractional-order finite control set model predictive control and the traditional PI dual closed-loop control and fractional-order finite control set model predictive control in the embodiments of the present invention. Among them, (a), (b) and (c) represent the comparison diagrams of voltage waveforms under the traditional PI dual closed-loop control, fractional-order finite control set model predictive control and improved fractional-order finite control set model predictive control, respectively.

[0033] Explanation of the labels in the diagram: V g For AC input voltage, I g For AC input current, V Co This represents the DC-side output voltage. IOMPC is an integer-order model predictive control, FOMPC is a fractional-order model predictive control, and TDC-FOMPC is an improved fractional-order model predictive control. Detailed Implementation

[0034] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0035] This invention proposes an improved fractional-order finite control set model predictive control method for a single-phase quasi-Z source rectifier, which aims to enable the rectifier to operate at unity power factor and improve its steady-state and dynamic performance.

[0036] The fractional-order finite control set model control method based on a single-phase quasi-Z-source rectifier includes the following steps:

[0037] In step S101, the AC input current, DC quasi-Z source network inductor current, and capacitor voltage are selected as control targets. The operating characteristics of the single-phase quasi-Z source rectifier are analyzed. Based on the analysis of the AC side circuit, the state equations corresponding to the AC input current in different states of the rectifier bridge switch are obtained. At the same time, in order to reduce the distortion rate of the AC input current and improve the power quality, a fractional-order calculus operator is introduced, and the fractional-order state equations are used to replace the integer-order state equations. Based on the modal analysis of the DC side equivalent circuit, the state equations of the quasi-Z source network inductor current and capacitor voltage in different circuit states are obtained. When the circuit is in a non-shoo-through state, the switch is turned on; when the circuit is in a shoot-through state, the switch is turned off.

[0038] In step S102, the state equations of the selected inductor current and capacitor voltage are discretized using the forward Euler method to obtain the prediction models for the required current and voltage. The possible combinations of switching states of the rectifier bridge switch and the quasi-Z-source network switch are listed, and these combinations are mapped to the prediction models to construct the associated multi-objective control value function. The reference value of the input AC current is obtained by multiplying the phase of the quasi-Z-source network capacitor voltage by the phase of the input AC voltage through the outer loop output of the PI control. The reference value of the quasi-Z-source network inductor current is obtained by outputting the output DC voltage through the outer loop of the PI control. Considering the delay in the sampling and calculation process, the control method is improved through a two-step prediction method, adding delay time compensation.

[0039] In step S103, the required voltage and current are sampled by voltage and current sensors. Based on the sampled values ​​and calculated values, predictive control is performed by an improved fractional-order finite control set model. The optimal switching state combination is obtained by iterative optimization to minimize the value function. Based on the optimal switching state combination, the switching states of the rectifier bridge switch and the switch in the quasi-Z source network are determined.

[0040] Based on the topology of the single-phase quasi-Z-source rectifier, analyzing the AC side circuit, the state equation for the AC side input current can be obtained as follows:

[0041]

[0042] Among them, L g For AC side filter inductance; i g u g u pn These represent the AC input current, AC input voltage, and rectifier bridge output voltage, respectively; S1 represents different operating modes of the rectifier, which are related to the different switching states of the rectifier bridge switching transistors, and takes the value of -1, 0, or 1.

[0043] To improve power quality, reduce the distortion rate of the AC input current, and increase control flexibility, a fractional-order calculus operator α is introduced, and the above state equation becomes:

[0044]

[0045] Discretizing the above equations using the forward Euler method yields:

[0046]

[0047] Among them, i g (k+1) and i g (k) represents the AC input current value at sampling times k+1 and k, respectively; T s To control the cycle.

[0048] Combining the two equations above, the fractional-order prediction expression for the AC input current can be obtained as follows:

[0049]

[0050] Among them, u g (k) and u pn (k) represents the AC input voltage and rectifier bridge output voltage at the k-th sampling time, respectively.

[0051] Analyzing the DC side of a single-phase quasi-Z-source rectifier, the mathematical model of its equivalent circuit can be obtained as follows:

[0052]

[0053] Where the left-hand side is the state equation for the circuit in the direct-flow state, and the right-hand side is the state equation for the circuit in the non-direct-flow state. L1 and L2 are the inductors in the quasi-Z-source network, and C1 and C2 are the capacitors in the quasi-Z-source network. o R is the output capacitor, and R is the output load.

[0054] Based on the mathematical model of the DC side of the single-phase quasi-Z source rectifier described above, the state equation for the current in inductor L2 can be obtained as follows:

[0055]

[0056] Among them, i L2 u C1 u C2 and u Co These represent the current in the quasi-Z source network inductor L2, the voltage across the quasi-Z source network capacitors C1 and C2, and the output capacitor C, respectively. oThe voltage; S2 is the switching state of the quasi-Z source network switch, with a value of 0 or 1. When the rectifier is in the shoot-through state, S2 has a value of 0, and when the rectifier is in the non-shoot-through state, S2 has a value of 1.

[0057] Similarly, based on the state equation of the current, using the forward Euler method, the predicted expression for the current of the quasi-Z source network inductor L2 can be obtained as follows:

[0058]

[0059] Among them, i L2 (k+1) and i L2 (k) represents the inductor current value of the quasi-Z source network at sampling times k+1 and k, respectively; u C2 (k), u C1 (k) and u Co (k) represents the quasi-Z source network capacitor voltage value and output DC voltage value at the k-th sampling time, respectively; S2 is the same as above.

[0060] The state equation for the voltage across capacitor C2 is:

[0061]

[0062] Among them, i L1 ,i L2 and i pn S1 represents the current of the quasi-Z source network inductor L1, the current of the quasi-Z source network inductor L1, and the output current of the rectifier bridge, respectively; S2 is the same as above.

[0063] Similarly, based on the state equation of voltage, using the forward Euler method, the predicted expression for the voltage of capacitor C2 in the quasi-Z source network can be obtained as follows:

[0064]

[0065] Among them, u C2 (k+1) and u C2 (k) represents the capacitor voltage values ​​of the quasi-Z source network at sampling times k+1 and k, respectively; i pn (k), i L2 (k) and i L1 (k) represents the output current value of the rectifier bridge and the inductor current value of the quasi-Z source network at the k-th sampling time, respectively; S2 is the same as above.

[0066] The single-phase quasi-Z source rectifier has five switching transistors. Different combinations of switching states of the transistors correspond to different current and voltage prediction models through different values ​​of S1 and S2. The correspondence is shown in Table 1.

[0067] Table 1 Correspondence Table

[0068] Serial number n 1 2 3 4 5 6 7 8 9 Switching state 01101 10011 01011 10101 01110 10110 11010 11100 11110 S1 -1 1 0 0 0 0 0 0 0 S2 1 1 1 0 0 0 0 0 0

[0069] To evaluate the predicted current and voltage values ​​under different combinations of switching states, a value function for multi-objective control associated with the controlled voltage and current is constructed as follows:

[0070]

[0071] Among them, i g (k+1),i L2 (k+1) and u C2 (k+1) represent the AC input current value and the inductor current and capacitor voltage values ​​of the quasi-Z source network at the (k+1)th sampling time, respectively; λ is a weighting factor, which is a fixed constant, and its magnitude can highlight the focus of multi-objective control; i gref 、i L2ref and u C2ref These represent the reference values ​​for the AC input current, the quasi-Z source network inductor current, and the capacitor voltage, respectively. Where i gref The voltage is obtained by multiplying the outer loop output of the voltage of capacitor C2 in the quasi-Z source network controlled by a PI controller with the phase of the AC input voltage, to achieve unity power factor operation; L2ref It is obtained from the outer loop output of the DC output voltage controlled by a PI controller; u C2ref It is a fixed constant.

[0072] By minimizing the value function, the optimal state combination of the five switches can be obtained, and at each time step, a fractional-order prediction model can be used for rolling optimization, thereby controlling each switch in real time and achieving the goal of the rectifier operating at unity power factor and outputting a stable DC voltage.

[0073] Considering the one-step delay characteristic of fractional-order model predictive control (FIGDC), meaning the optimal switching state combination of the switching transistors obtained in the current cycle will be applied to the switching transistors at the beginning of the next cycle, and this control delay affects the control accuracy and performance of the algorithm, a two-step prediction method is adopted to compensate for the control delay inherent in this method, combining one-step prediction with fractional-order model prediction. This means the optimal switching state applied at this moment is obtained in the previous cycle. Taking the AC input current as an example, at t... k At time tk, the optimal switching state obtained in the previous cycle is applied to this moment, and the AC input current value i at time tk is sampled. g (k), based on the sampled values ​​and switching states, the AC input current value i at time t(k+1) is calculated using a prediction model. g (k+1), in i gBased on (k+1), a further prediction is made, using the value function of multi-objective control to predict the value i at time t(k+2) under different switching states. g The optimal switching state is evaluated at (k+2) and applied to the system at the start of the next sampling period, i.e., time t(k+1). The fractional-order model predictive control algorithm with time delay compensation exhibits better real-time control performance.

[0074] Understandably, ideally, the sampling of the required voltage and current values, the calculation of the prediction model, and the optimization process of the value function for multi-objective control all occur within t. k The optimal combination of switching states obtained at the sampling time is applied to the switching transistor, thereby achieving the desired state at time t. k+1 At the sampling time, the error between the actual value of the control target and the given reference value is minimized. However, in practical digital signal processing systems, both sampling and computation require time, and the optimal switching state obtained within this period is only applied to the switching transistor when the next sampling period arrives. Therefore, model predictive control has a one-cycle delay, which affects control accuracy.

[0075] To address the aforementioned time delay issue, a two-step prediction method is used for delay compensation. An improved fractional-order model predictive control based on a time delay compensation strategy is employed to control the single-phase quasi-Z-source rectifier. The specific process is as follows: at t... k The optimal combination of switching states obtained in the (k-1)th sampling period will be applied to the switching transistor at time t. k Given the required current and voltage values ​​at time t, and based on the voltage values ​​and optimal switching state, the control objective at time t can be obtained through a predictive expression. k+1 The predicted value at time t. Further prediction yields the value at time t. k+2 The predicted values ​​of the controlled current and voltage under different switching states at time t and their corresponding value functions are used to select the switching state that minimizes the value function as the optimal switching state. k+1 It acts on each switching transistor in the circuit at all times.

[0076] Based on the above principle, the controlled current and voltage at time t k+2 The prediction model at time t can be derived from its value at time t. k+1 A time-based prediction model has been developed.

[0077] AC input current at t k+2 The prediction model for time is:

[0078]

[0079] The current in inductor L2 of the quasi-Z source network is at t k+2 The prediction model for time is:

[0080]

[0081] The voltage of capacitor C2 in the quasi-Z source network is at t k+2 The prediction model for time is:

[0082]

[0083] Using the following value function, select in advance at t k+1 Optimal switching state at any given time:

[0084]

[0085] As can be seen from the defined value function, the smaller the value function, the smaller the error between the sampled values ​​of the controlled current and voltage and the given reference current and voltage values. Therefore, the optimal switching state combination, which minimizes the value function, should be selected to control the five switches in the circuit.

[0086] like Figure 1 The diagram shows the topology of a single-phase quasi-Z-source rectifier in an embodiment of the present invention, including an input AC power supply and filter inductor, a rectifier bridge, a quasi-Z-source network, and an output filter capacitor and load. The rectifier allows for short-term shoot-through of the bridge arms, exhibits high circuit reliability, and can achieve buck-boost conversion. When the circuit is in shoot-through mode, switch S5 in the quasi-Z-source network is off; when the circuit is in non-shoot-through mode, switch S5 is on.

[0087] like Figure 2 The diagram shown is a control block diagram of an improved fractional-order model predictive control for a single-phase quasi-Z-source rectifier according to an embodiment of the present invention. C2ref u Coref 、i gref and i L2ref These are the reference values ​​for the voltage of capacitor C2, output voltage, AC input current, and current of inductor L2 in the quasi-Z source network, respectively; g (k), i L2 (k) and u C2 (k) represents t k The AC input current, the current of the quasi-Z source network inductor L2, and the voltage value of the quasi-Z source network capacitor C2 at each moment; g (k+1),i L2 (k+1) and u C2 (k+1) represent t k+1 The AC input current, the current of the quasi-Z source network inductor L2, and the voltage value of the quasi-Z source network capacitor C2 at each moment; g (k+2), i L2 (k+2) and u C2(k+2) represent t k+2 The AC input current, the current of the quasi-Z source network inductor L2, and the voltage value of the quasi-Z source network capacitor C2 at each moment; g n (x) represents the value function value under different switching state combinations. As shown in the figure, the reference value of the AC input current is obtained by multiplying the output of the quasi-Z source network capacitor C2 voltage outer loop under PI control with the phase of the AC input voltage. The reference value of the quasi-Z source network inductor L2 current is obtained by the output of the output side DC voltage outer loop under PI control. Based on the sampled and calculated values, the optimal switching state for each sampling period is obtained through the proposed fractional-order model prediction control based on two-step prediction improvement, controlling the opening and closing of the five switching transistors in the control circuit.

[0088] like Figure 3 The diagram shown illustrates the main flow of the improved fractional-order model predictive control for a single-phase quasi-Z-source rectifier in this embodiment of the invention. The process includes the following steps: defining the switching state combinations and other parameters of the five switching transistors in the circuit; sampling the required current and voltage in the k-th sampling period to obtain discretized current and voltage; obtaining a predictive model by adding delay compensation; evaluating the correlation value function composed of reference and predicted values ​​of current and voltage under different switching states to obtain the switching state combination that minimizes the value function; and applying the optimal switching state combination to the rectifier.

[0089] like Figure 4 The diagram shown is a simplified schematic of the delay compensation strategy for fractional-order finite control set model predictive control in this embodiment of the invention. Ideally, the sampling and calculation of the model predictive control method, as well as the optimization of the switching states, are completed instantaneously, achieving the control objective of minimizing the error between the actual and reference values ​​of the controlled current and voltage. However, the sampling and calculation of current and voltage require time, such as... Figure 4 As shown in (a), at t k Sampling takes time, and the optimal switching state C3 is only applied to the circuit after the sampling and calculation time delay. This results in a deviation between the predicted and actual current and voltage values ​​at the beginning of the next switching cycle, leading to a decrease in the system's control accuracy. Furthermore, the obtained optimal switching state is actually only applied to the system at the arrival of the next sampling cycle. Figure 4 As shown in (b), S3 at t k+1 Since the optimal switching state obtained in the previous sampling period only takes effect at the beginning of the next sampling period, traditional model predictive control algorithms suffer from a one-cycle time delay, which affects the algorithm's control accuracy. Therefore, an improved fractional-order model predictive control based on a two-step prediction method is adopted, such as... Figure 4 As shown in (c), at t kThe optimal combination of switching states obtained in the (k-1)th sampling period will be applied to the switching transistor at time t. k Given the required current and voltage values ​​at time t, and based on the voltage values ​​and optimal switching state, the control objective at time t can be obtained through a predictive expression. k+1 The predicted value at time t. Further prediction yields the value at time t. k+2 The predicted values ​​of the controlled current and voltage under different switching states at time t and their corresponding value functions are used to select the switching state that minimizes the value function as the optimal switching state. k+1 This is applied to each switch in the circuit at all times. This compensates for the control delay and improves the control accuracy of the algorithm.

[0090] like Figure 5 As shown in (a)-(d), both fractional-order model predictive control and the improved fractional-order model predictive control based on a delay compensation strategy can achieve in-phase AC voltage and current, enabling the rectifier to operate at unity power factor and stabilizing the DC output voltage at 320V. Before adopting the delay compensation strategy, the total harmonic distortion (THD) of the AC current was 2.51% of the fundamental current, and the DC output voltage fluctuation was 0.35V. After adopting the delay compensation strategy, the THD of the AC current was 2.10%, and the DC output voltage fluctuation was 0.175V. It can be seen that after adopting the delay compensation strategy, the THD of the AC current is reduced, the DC output voltage fluctuation is decreased, and the steady-state performance of the system is improved.

[0091] like Figure 6 As shown in (a)-(b), the THD of the AC side current increases with increasing input voltage and decreases with increasing output power. Furthermore, it can be seen that fractional-order model predictive control (FEM) outperforms integer-order model predictive control (IMC), and the improved FEM outperforms FEM. This demonstrates that the improved FEM based on a delay compensation strategy is more effective in harmonic compensation, reducing harmonic pollution on the AC side and achieving better power quality.

[0092] like Figure 7As shown in (a)-(c), when the setpoint of the DC-side output voltage suddenly increases, the amplitude of the AC-side input current increases; when the setpoint of the DC-side output voltage suddenly decreases, the amplitude of the AC-side input current decreases. During the period of DC voltage change, the phase of the AC-side input current remains the same as the phase of the input voltage, making the rectifier's output voltage adjustable and exhibiting good dynamic performance. The figures show that under dual-loop control, the DC-side output voltage requires 0.4s to achieve stable tracking of the reference value; under fractional-order model predictive control, it requires 0.14s; and under improved fractional-order model predictive control based on a delay compensation strategy, it requires 0.07s. Therefore, using improved fractional-order model predictive control, the system has the fastest dynamic response and can reach steady state in the shortest time.

[0093] According to the fractional-order finite control set model control method based on a single-phase quasi-Z-source rectifier proposed in this embodiment, reference values ​​for the input AC current and the quasi-Z-source network inductor current are obtained by performing PI control on the capacitor voltage and output DC voltage of the quasi-Z-source network respectively. To reduce the distortion rate of the AC input current, a fractional-order calculus operator is introduced into the prediction model of the input inductor current. The state equation is discretized using the forward Euler method to obtain prediction models for the input AC current, the capacitor voltage and inductor current of the quasi-Z-source network, and establish associated multi-control objective value functions. Based on the influence of sampling and calculation delay time on control accuracy, a two-step prediction method is used to compensate for the time delay of the control strategy. The required voltage and current are sampled, and the value functions under different switching states are solved based on all predefined switching states. By iterative optimization, the switching state that minimizes the value function is determined as the optimal switching state. The optimal switching state is used to control the switching transistors of the rectifier bridge and the quasi-Z-source network to achieve the control objective. Implementing this invention can achieve the following effects: compared with traditional integer-order finite set model predictive control and fractional-order finite set model predictive control without delay compensation, it reduces the input-side AC current distortion rate, reduces the output-side DC voltage fluctuation, improves the dynamic response speed of the output-side DC voltage, and has higher control accuracy.

[0094] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0095] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0096] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

Claims

1. A fractional-order finite control set model control method based on a single-phase quasi-Z-source rectifier, characterized in that, Includes the following steps: The AC input current, DC quasi-Z source network inductor current, and capacitor voltage are selected as control targets. The operating characteristics of the single-phase quasi-Z source rectifier are analyzed. Based on the analysis of the AC side circuit, the state equations corresponding to the AC input current under different states of the rectifier bridge switch are obtained. Fractional calculus operators are introduced, and fractional state equations are used to replace integer state equations. Based on the modal analysis of the DC equivalent circuit, the state equations of the quasi-Z source network inductor current and capacitor voltage under different circuit states are obtained. When the circuit is in a non-shoo-through state, the switch is turned on; when the circuit is in a shoot-through state, the switch is turned off. Using the forward Euler method, the state equations of the selected inductor current and capacitor voltage are discretized to obtain the prediction models of the required current and voltage. The possible combinations of switching states of the rectifier bridge switch and the quasi-Z source network switch are listed. The combinations are mapped to the prediction models to construct the value function of the associated multi-objective control and to compensate for the delay time. The required voltage and current are sampled by voltage and current sensors. Based on the sampled and calculated values, predictive control is performed using an improved fractional-order finite control set model. Through traversal optimization, the switching state combination that minimizes the value function is obtained and determined as the optimal switching state combination. Based on the optimal switching state combination, the switching states of the rectifier bridge switch and the switches in the quasi-Z source network are determined.

2. The method according to claim 1, characterized in that, The AC side circuit of a single-phase quasi-Z-source rectifier is analyzed. A fractional-order calculus operator is introduced to obtain the state equation of the input AC current. The fractional-order state equation is discretized using the forward Euler method, yielding the fractional-order prediction expression of the input AC current: Among them, i g (k+1) and i g (k) represents the AC input current value at sampling times k+1 and k, respectively; T s For control cycle; L g This refers to the AC side inductance value; u g (k) and u pn (k) represents the AC input voltage and rectifier bridge output voltage at the k-th sampling time, respectively; α is the order of the introduced fractional calculus operator; S1 is the rectifier's operating mode, with a value of -1, 0, or 1; The DC-side circuit of a single-phase quasi-Z-source rectifier is analyzed to obtain the state equations for the capacitor voltage and inductor current of the quasi-Z-source network. The state equations are then discretized using the forward Euler method, yielding the predicted expression for the inductor current: Among them, i L2 (k+1) and i L2 (k) represents the inductor current value of the quasi-Z source network at sampling times k+1 and k, respectively; u C2 (k), u C1 (k) and u Co (k) represents the quasi-Z source network capacitor voltage and output DC voltage at the k-th sampling time, respectively; S2 is the switching state of the quasi-Z source network switch, which takes the value of 0 or 1. When the rectifier is in the shoot-through state, S2 takes the value of 0, and when the rectifier is in the non-shoot-through state, S2 takes the value of 1. The predicted expression for the capacitor voltage is obtained as follows: Among them, u C2 (k+1) and u C2 (k) represents the capacitor voltage values ​​of the quasi-Z source network at sampling times k+1 and k, respectively; i pn (k), i L2 (k) and i L1 (k) represents the output current value of the rectifier bridge and the inductor current value of the quasi-Z source network at the k-th sampling time, respectively.

3. The method according to claim 1, characterized in that, The value function for constructing a multi-objective control system with correlation is: Among them, i g (k+1),i L2 (k+1) and u C2 (k+1) represent the AC input current value and the inductor current and capacitor voltage values ​​of the quasi-Z source network at the (k+1)th sampling time, respectively; λ is the weighting factor; i gref i L2ref and u C2ref These represent the reference values ​​for the AC input current, the quasi-Z source network inductor current, and the capacitor voltage, respectively; i gref The voltage is obtained by multiplying the output of the outer loop of the quasi-Z source network capacitor voltage controlled by PI with the phase of the AC side input voltage, to achieve unity power factor operation; L2ref It is obtained from the output of the outer loop of the DC output voltage controlled by a PI controller; u C2ref It is a fixed constant.

4. The method according to claim 1, characterized in that, Compensation for delay time includes: A two-step prediction method is adopted to compensate for control delay by combining one-step prediction with fractional-order model prediction. That is, the optimal switching state at this moment is obtained in the previous cycle.

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