Three-level converter two-dimensional optimization model prediction control method and device

Abstract

The invention discloses a three-level converter two-dimensional optimization model prediction control method and device. The method comprises the following steps: calculating a reference voltage vector and a phase angle of an inverter at a moment k through a dead-beat principle; the method comprises the following steps: dividing a voltage vector region of a three-level converter into six large sectors based on a two-dimensional optimization principle, and dividing each large sector into six small sectors; taking the optimal voltage vectors corresponding to the small and medium sectors in each large sector as preparatory optimal voltage vectors, and making a preparatory optimal voltage vector corresponding table of each sector; selecting a preparatory optimal voltage vector corresponding to the reference voltage vector at the k moment by using the preparatory optimal voltage vector corresponding table of each sector; determining an optimal voltage vector according to the prepared optimal voltage vector and the neutral-point voltage; according to the invention, on the premise of realizing neutral-point voltage balance and stable operation of the three-level converter, current ripples and calculation burden can be reduced.

Description

Technical Field

[0001] The present invention belongs to the field of three-level inverter control, and in particular relates to a two-dimensional optimization model predictive control method and device for a three-level converter. Background Art

[0002] As the proportion of new energy sources in the power system increases, the inertia of the power system decreases and the stability decreases. Therefore, additional energy storage equipment is needed to improve the stability of the system and make up for the intermittent and uncertain shortcomings of renewable energy generation. As the interface between photovoltaic, wind power and energy storage batteries and traditional power grids, power electronic converters play a key role in the efficiency and stability of the entire system. Compared with two-level converters, three-level converters have the advantages of large capacity, high voltage level, and good output current quality, and are widely used in medium-voltage and high-power applications.

[0003] Model predictive control has many advantages, including fast dynamic response, high flexibility, good robustness, easy design and tuning, and the ability to handle multivariable constraints and implement multivariable control in a single control loop. The basic principle of model predictive control is to calculate the cost function value of each feasible voltage vector in each control cycle, and use the voltage vector corresponding to the minimum cost function value as the optimal voltage vector to be implemented in the next control cycle. The above principle leads to a large amount of calculation for model predictive control, and the amount of calculation increases exponentially with the number of levels of the power electronic converter, which limits the large-scale promotion and low-cost popularization of model predictive control.

[0004] In order to solve the above problems, there is an urgent need to develop an efficient and low-computation model predictive control method. Summary of the invention

[0005] The object of the present invention is to provide a two-dimensional optimization model predictive control method and device for a three-level converter. The method is used to solve the problem of high algorithm calculation in the traditional model predictive control method. The two-dimensional optimization method proposed in the present invention can avoid the enumeration of voltage vectors and can effectively reduce the calculation burden of the algorithm while retaining the original advantages of model prediction.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] The present invention provides a two-dimensional optimization model predictive control method for a three-level converter, characterized in that the method comprises:

[0008] Collecting state variables of the T-type three-level converter at time k; the state information includes grid current, grid voltage, and upper and lower bus capacitor voltages on the DC side;

[0009] A discrete mathematical model of the three-level converter in a two-phase stationary α-β coordinate system is established, and based on the Lagrange extrapolation theorem, the reference value of the grid current at time k+1 in the α-β coordinate system is obtained;

[0010] The reference voltage vector and its phase angle of the inverter at time k are calculated by the deadbeat principle;

[0011] The voltage vector region of the three-level converter is divided into sectors based on the two-dimensional optimization principle;

[0012] The corresponding optimal voltage vector in the divided sectors is used as a preliminary optimal voltage vector, and a corresponding table of preliminary optimal voltage vectors of each sector is prepared;

[0013] Using the corresponding table of the prepared optimal voltage vectors of each sector, the prepared optimal voltage vector corresponding to the reference voltage vector at time k is selected;

[0014] Determine the optimal voltage vector according to the prepared optimal voltage vector and the midpoint voltage;

[0015] The gate driving signal corresponding to the optimal voltage vector is applied to the power electronic semiconductor device of the three-level converter.

[0016] The corresponding gate drive signal is applied to the power electronic semiconductor device of the T-type three-level energy storage converter.

[0017] In one implementation, the discrete mathematical model of the T-type three-level energy storage converter in a two-phase static α-β coordinate system is:

[0018]

[0019] In the formula, L and R are the inductance value and equivalent resistance of the filter inductor respectively, T s is the sampling period; i α (k), i β (k) is the grid current sampling value in the α-β coordinate system at time k; i α (k+1), i β (k+1) is the predicted value of the grid current in the α-β coordinate system at time k+1; e α (k), e β (k) is the grid voltage sampling value in the α-β coordinate system at time k; u α (k),u β (k) is the AC side output voltage of the converter in the α-β coordinate system at time k, u o (k),u o (k+1) are the DC side midpoint voltage values ​​at time k and k+1 respectively; i o(k) is the midpoint current value of the DC side at time k; C1 and C2 are the capacitance values ​​of the upper and lower bus capacitors of the DC side respectively; T s is the control period of the control system.

[0020] In one implementation, the reference value of the grid current at time k+1 calculated according to the Lagrange extrapolation theorem is:

[0021]

[0022] In the formula, are the reference values ​​of the α-axis grid current at the time k+1, k, k-1, and k-2 respectively, are the reference values ​​of the β-axis grid current at moments k+1, k, k-1, and k-2 respectively.

[0023] In one implementation, the deadbeat principle makes the predicted value of the grid-side current at time k+1 equal to the grid-side current reference value obtained by Lagrange extrapolation theorem, and then calculates the reference voltage vector of the inverter at time k as:

[0024]

[0025] In the formula, They are the components of the reference voltage on the α and β axes respectively.

[0026] The phase angle θ of the reference voltage vector is:

[0027]

[0028] In one implementation, the voltage vector region of the three-level converter is divided into six large sectors, each of which is further divided into six small sectors; the six large sectors are divided according to the magnitude of the reference voltage vector phase angle, and the small sectors are obtained by rotating the reference voltage vector to the first large sector to obtain an equivalent phase angle, and are divided according to the magnitude of the equivalent phase angle;

[0029] In the α-β coordinate system, the reference voltage vector By positioning the voltage vector in two dimensions, the β-axis and the α-axis, the reference voltage vector can be expressed as:

[0030]

[0031] where u ref and θ ref are the amplitude and phase angle of the reference voltage respectively;

[0032] The relationship between the 6 large sectors and the reference voltage vector phase angle is:

[0033] The first largest sector corresponds to a phase angle of 0<θ ref ≤π / 3;

[0034] The second largest sector corresponds to a phase angle of π / 3<θ ref ≤2π / 3;

[0035] The phase angle corresponding to the third largest sector is 2π / 3<θ ref ≤π;

[0036] The phase angle corresponding to the fourth largest sector is -π<θ ref ≤-2π / 3;

[0037] The phase angle corresponding to the fifth largest sector is -2π / 3<θ ref ≤-π / 3;

[0038] The phase angle corresponding to the sixth largest sector is -π / 3<θ ref ≤0.

[0039] In order to reduce the computational complexity of the two-dimensional optimization model predictive control method for three-level converters, the reference voltage vector is rotated to the first largest sector, and the equivalent phase angle is:

[0040] θ refR =θ ref -(n-1)*π / 3

[0041] Where n is the large sector where the reference voltage is located;

[0042] Equivalent reference voltage vector for:

[0043]

[0044] The relationship between the 6 small sectors and the equivalent reference voltage vector is:

[0045] Phase angle limit 0≤θ refR ≤π / 6;

[0046] Boundary restrictions Corresponding to the Ith small sector;

[0047] Boundary restrictions and Corresponding to the IVth small sector;

[0048] Boundary restrictions and Corresponding to the Vth small sector;

[0049] Boundary restrictions and Corresponding to the second small sector;

[0050] Boundary restrictions Corresponding to the Ith small sector;

[0051] Boundary restrictions and Corresponding to the VIth small sector;

[0052] Boundary restrictions and Corresponding to the Vth small sector;

[0053] Boundary restrictions and Corresponding to the third small sector.

[0054] In one implementation, the zero voltage vector only considers the "000" state, i.e., V1, and the optimal voltage vector corresponding to the small sector in each large sector is:

[0055] In the first large sector, the optimal voltage vector of the first small sector is V1, the optimal voltage vector of the second small sector is V4 and V5, the optimal voltage vector of the third small sector is V6 and V7, and the optimal voltage vector of the fourth small sector is V 22 、The optimal voltage vector of the Vth small sector is V 16 、The optimal voltage vector of the VIth small sector is V 23 ;

[0056] In the second largest sector, the optimal voltage vector of the first small sector is V1, the optimal voltage vector of the second small sector is V6 and V7, the optimal voltage vector of the third small sector is V8 and V9, and the optimal voltage vector of the fourth small sector is V 23 、The optimal voltage vector of the Vth small sector is V 17 、The optimal voltage vector of the VIth small sector is V 24 ;

[0057] In the third largest sector, the optimal voltage vector of the first small sector is V1, the optimal voltage vector of the second small sector is V8 and V9, and the optimal voltage vector of the third small sector is V 10 and V 11 , the optimal voltage vector of the IVth small sector is V 24 、The optimal voltage vector of the Vth small sector is V 18 、The optimal voltage vector of the VIth small sector is V 25 ;

[0058] In the fourth largest sector, the optimal voltage vector of the first small sector is V1, and the optimal voltage vector of the second small sector is V 10 and V 11 , the optimal voltage vector of the third small sector is V 12 and V 13 , the optimal voltage vector of the IVth small sector is V 25 、The optimal voltage vector of the Vth small sector is V 19 、The optimal voltage vector of the VIth small sector is V 26 ;

[0059] In the fifth largest sector, the optimal voltage vector of the first small sector is V1, and the optimal voltage vector of the second small sector is V 12 and V 13 , the optimal voltage vector of the third small sector is V 14 and V 15 , the optimal voltage vector of the IVth small sector is V 26 、The optimal voltage vector of the Vth small sector is V 20 、The optimal voltage vector of the VIth small sector is V 27 ;

[0060] In the sixth largest sector, the optimal voltage vector of the first small sector is V1, and the optimal voltage vector of the second small sector is V 14 and V 15 The optimal voltage vectors for the third small sector are V4 and V5, and the optimal voltage vector for the fourth small sector is V 27 、The optimal voltage vector of the Vth small sector is V 21 、The optimal voltage vector of the VIth small sector is V 22 .

[0061] In one implementation, determining the optimal voltage vector according to the prepared optimal voltage vector and the midpoint voltage includes:

[0062] When the prepared optimal voltage vector is zero, medium, or large voltage vector, the prepared optimal voltage vector is the optimal voltage vector;

[0063] When the prepared optimal voltage vector is a small voltage vector, the optimal voltage vector is selected from the prepared optimal voltage vectors in combination with the midpoint voltage.

[0064] In one implementation, the small voltage vector is divided into a P-type voltage vector and an N-type voltage vector; the P-type voltage vector can increase the midpoint voltage, and the N-type voltage vector can reduce the midpoint voltage;

[0065] Combined with the dynamic mathematical model of the DC side midpoint voltage, it is concluded that when the midpoint voltage is greater than zero, the N-type voltage vector is selected; when the midpoint voltage is less than zero, the P-type voltage vector is selected.

[0066] The present invention also provides a two-dimensional optimization model predictive control device for a three-level converter, the device comprising:

[0067] A collection module, used to collect state variables of the T-type three-level energy storage converter at time k; the state variables include grid current, grid-side voltage and capacitor voltage;

[0068] The discrete mathematical model module is used to establish a discrete mathematical model of the T-type three-level energy storage converter in a two-phase static α-β coordinate system, and obtain the reference value of the grid current at time k+1 according to the Lagrange extrapolation theorem;

[0069] A calculation module, used to calculate the reference voltage vector and its phase angle of the inverter at time k by using the deadbeat principle;

[0070] A two-dimensional optimization voltage vector sector division module is used to divide the voltage vector region of the three-level converter into sectors based on the two-dimensional optimization principle;

[0071] A preparatory optimal voltage vector table module is used to use the optimal voltage vectors corresponding to the small and medium sectors of each large sector as preparatory optimal voltage vectors, and to prepare a corresponding table of preparatory optimal voltage vectors for each sector;

[0072] The module for determining the preliminary optimal voltage vector selects the preliminary optimal voltage vector corresponding to the reference voltage vector at time k by using the preliminary optimal voltage vector corresponding table of each sector;

[0073] An optimal voltage vector confirmation module is used to determine the optimal voltage vector according to the prepared optimal voltage vector and the midpoint voltage;

[0074] The action module is used to apply the optimal voltage vector and the gate drive signal corresponding to the action time thereof to the power electronic semiconductor device of the T-type three-level energy storage converter.

[0075] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0076] The present invention can reduce current ripple and improve current quality under the premise of achieving midpoint voltage balance and stable operation of the three-level energy storage converter. The proposed hybrid model predictive control is different from the traditional model predictive control with discrete output characteristics. The optimal voltage vector is executed at the beginning of the control cycle. Instead, it is switched at a specific time in the control cycle through calculation, and a time-continuous output characteristic is obtained, which improves the current quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The accompanying drawings are part of the present invention and are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but do not constitute an improper limitation of the present invention. Obviously, the accompanying drawings described below are only some embodiments. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0078] Figure 1 A topological diagram of a three-level converter provided in an embodiment of the present invention;

[0079] Figure 2 A voltage vector distribution diagram of a three-level converter provided in an embodiment of the present invention;

[0080] Figure 3A flow chart of a two-dimensional optimization model predictive control method for a three-level converter provided in an embodiment of the present invention;

[0081] Figure 4 A voltage vector sector division diagram of an unimproved three-level converter provided in an embodiment of the present invention;

[0082] Figure 5 A voltage vector region diagram of a three-level storage converter to be improved provided in an embodiment of the present invention;

[0083] Figure 6 A voltage vector sector division diagram based on two-dimensional optimization for a three-level converter provided in an embodiment of the present invention;

[0084] Figure 7 It is a comparison diagram of steady-state simulation waveforms of the finite set model predictive control and the two-dimensional optimization model predictive control provided in the embodiment of the present invention, wherein Figure 7 (a) is the steady-state waveform of the finite set model predictive control, Figure 7 (b) is the steady-state waveform of the two-dimensional optimization model predictive control;

[0085] Figure 8 It is a comparison diagram of dynamic simulation waveforms of finite set model predictive control and two-dimensional optimization model predictive control provided in the embodiment of the present invention, wherein Figure 8 (a) is the dynamic waveform of the finite set model predictive control. Figure 8 (b) is the dynamic waveform of the two-dimensional optimization model predictive control.

[0086] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0087] In order to deepen the recognition and understanding of the present invention, the technical solution of the present invention is further introduced below in conjunction with the accompanying drawings and specific implementation methods. The following embodiments are explanations of the present invention and the present invention is not limited to the following embodiments.

[0088] It should be clear that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] Figure 1The topology diagram of the T-type three-level energy storage converter is given. Each phase bridge arm consists of four power electronic semiconductor devices, and the DC side is composed of capacitors C1 and C2 in series, and the parameters of C1 and C2 are consistent. The midpoint of the DC side is defined as o, and the potential is zero. The grid-side filtering link consists of a single L-type filter, and the resistor R is the equivalent resistance of the filter. x ,i x are the grid voltage and current respectively, (x=a,b,c). c1 and u c2 are the voltages of capacitors C1 and C2, u dc is a DC voltage. Each bridge arm of the T-type three-level energy storage converter has three effective switching states: S x ∈{1, 0, -1}. Assuming that the capacitor voltage is balanced, that is, the voltage of each capacitor is equal to half of the DC voltage, the output voltage generated by these three switching states is u xo ∈{u dc / 2,0,-u dc / 2}. Table 1 summarizes the relationship between the output voltage, switch state and gate signal generated by the converter.

[0090] Table 1 Relationship between output voltage, switch state and gate signal, x = (a, b, c)

[0091]

[0092] In order to establish the mathematical model of the T-type three-level energy storage converter, it is assumed that the power electronic semiconductor devices are ideal switching devices, and the nonlinear effects such as the on-state voltage drop and switching delay of the power electronic semiconductor devices are ignored.

[0093] According to Kirchhoff's voltage law, in the three-phase stationary coordinate system, the dynamic response of the grid current is:

[0094]

[0095] The switching states of the T-type three-level energy storage converter are 27 (3 3 ) species, such as Figure 2 As shown in the α-β coordinate system, these switching states generate a total of 19 non-redundant voltage vectors and 8 redundant voltage vectors. According to the amplitude of the output voltage, the voltage vector is divided into zero vector (V1-V3), small vector (V4-V 15 ), mid vector (V 16 -V 21 ) and large vector (V 22 -V 27 ). There is only one zero vector, and there are two redundant vectors; there are six small vectors, and each has one redundant vector; there are six medium vectors and six large vectors, and there is no redundant vector. The voltage vectors are shown in Table 2.

[0096] Table 2 Voltage vector table

[0097]

[0098] like Figure 3 As shown, the disclosed embodiment provides a hybrid model predictive control method for a T-type three-level energy storage converter, the method specifically comprising the following steps:

[0099] Step S100: collecting state variables of a T-type three-level energy storage converter at time k.

[0100] Specifically, the grid current i at time k is collected by voltage and current sensors. a (k), i b (k), i c (k), grid voltage e a (k), e b (k), e c (k), and capacitor voltage u c1 (k),u c2 (k).

[0101] Step S200: establishing a discrete mathematical model of a T-type three-level energy storage converter in a two-phase stationary α-β coordinate system, and obtaining a reference value of the grid current at time k+1 according to the Lagrange extrapolation theorem.

[0102] The three-phase stationary coordinate system can be converted into a two-phase stationary coordinate system through Clarke transformation. Clarke transformation is:

[0103]

[0104] The mathematical model of the three-level energy storage converter in the two-phase stationary coordinate system is:

[0105]

[0106] Through the forward Euler method, the discrete mathematical model of the T-type three-level energy storage converter in the two-phase stationary coordinate system is:

[0107]

[0108] In the formula, L and R are the inductance value and equivalent resistance of the filter inductor respectively, T s is the sampling period; i α (k), i β (k) is the grid current sampling value in the α-β coordinate system at time k; i α (k+1), i β (k+1) is the predicted value of the grid current in the α-β coordinate system at time k+1; e α(k), e β (k) is the grid voltage sampling value in the α-β coordinate system at time k; u α (k),u β (k) is the output voltage of the AC side of the converter in the α-β coordinate system at time k.

[0109]

[0110] The DC side midpoint voltage is:

[0111] u o =u c2 -u c1

[0112] Assuming that the capacitor voltages are equal and the grid currents are balanced, the dynamic process of the DC side midpoint voltage can be modeled as:

[0113]

[0114] i o is the DC side midpoint current, which is given by the following formula:

[0115] i o =|S abc |i abc

[0116] In the formula, |S abc |=[1-|S a |,1-|S b |,1-|S c |],i abc =[i a ,i b ,i c ].

[0117] The predicted value of the midpoint voltage is:

[0118]

[0119] In the formula, u o (k),u o (k+1) are the DC side midpoint voltage values ​​at time k and k+1 respectively; i o (k) is the midpoint current value of the DC side at time k; C1 and C2 are the capacitance values ​​of the upper and lower bus capacitors of the DC side respectively; T s is the control period of the control system.

[0120] According to Lagrange's extrapolation theorem, in the α-β coordinate system, the reference value of the grid current at time k+1 is:

[0121]

[0122] In the formula, in the formula, are the reference values ​​of the α-axis grid current at the time k+1, k, k-1, and k-2 respectively, are the reference values ​​of the β-axis grid current at moments k+1, k, k-1, and k-2 respectively.

[0123] Step S300: Calculate the reference voltage vector and its phase angle of the inverter at time k by using the deadbeat principle.

[0124] The deadbeat principle is to make the predicted value of the grid-side current at time k+1 equal to the grid-side current reference value obtained by Lagrange extrapolation theorem, and then calculate the reference voltage vector of the inverter at time k as:

[0125]

[0126] In the formula, They are the components of the reference voltage on the α and β axes respectively.

[0127] The phase angle θ of the reference voltage vector is:

[0128]

[0129] Step S400: dividing the voltage vector region of the three-level converter into sectors based on the two-dimensional optimization principle.

[0130] In the α-β coordinate system, for a reference voltage vector The voltage vector can be positioned in two dimensions, the β axis and the α axis. To facilitate the division of the two-dimensional sectors of the voltage vector, the regions are divided along the symmetry axes of the adjacent voltage vectors in the α axis and the β axis directions, as shown in Tables 3 and 4. Therefore, after determining the reference voltage vector, it is only necessary to position the reference voltage vector in accordance with Tables 3 and 4 to select the optimal voltage vector, without enumerating all feasible voltage vectors, which greatly reduces the amount of calculation. Figure 4 This is the voltage vector area division diagram based on the above analysis.

[0131] Table 3 β-axis coordinate area division

[0132]

[0133] Table 4 α axis coordinate area division

[0134]

[0135] Although the above two-dimensional sector division can effectively reduce the amount of calculation, its sector division has some shortcomings. For example, according to Table 3 and Table 4, V4 or V5 will be selected as the optimal voltage vector and implemented in the next control cycle. However, among all voltage vectors, V6 and V7 are closest to the reference voltage vector and should be used as the optimal voltage vector. The areas with the above problems are as follows Figure 5 As shown. To address the above problems, a voltage vector sector division based on two-dimensional optimization is proposed, as shown in Figure 6 shown.

[0136] In the embodiment of the present application, the voltage vector region of the three-level converter is divided into sectors based on the two-dimensional optimization principle, including:

[0137] The voltage vector region of the three-level converter is divided into 6 large sectors, each of which is further divided into 6 small sectors; the 6 large sectors are divided according to the magnitude of the reference voltage vector phase angle, and the small sectors are obtained by rotating the reference voltage vector to the first large sector to obtain an equivalent phase angle, and are divided according to the magnitude of the equivalent phase angle;

[0138] In the α-β coordinate system, the reference voltage vector By positioning the voltage vector in two dimensions, the β-axis and the α-axis, the reference voltage vector can be expressed as:

[0139]

[0140] In the formula, u ref and θ ref are the amplitude and phase angle of the reference voltage respectively.

[0141] The voltage vector diagram is divided into six large sectors, and the relationship between the sectors and the phase angle of the reference voltage vector is shown in Table 5.

[0142] Table 5: Large sector division

[0143] <![CDATA[θ ref ]]> Large sector <![CDATA[0<θ ref ≤π / 3]]> 1 <![CDATA[π / 3<θ ref ≤2π / 3]]> 2 <![CDATA[2π / 3<θ ref ≤π]]> 3 <![CDATA[-π<θ ref ≤-2π / 3]]> 4 <![CDATA[-2π / 3<θ ref ≤-π / 3]]> 5 <![CDATA[-π / 3<θ ref ≤0]]> 6

[0144] In order to reduce the computational complexity, the reference voltage will be uniformly rotated to sector 1, and the equivalent phase angle is:

[0145] θ refR =θ ref -(n-1)*π / 3

[0146] Where n is the large sector where the reference voltage is located.

[0147] Equivalent reference voltage vector for:

[0148]

[0149] In each large sector, six small sectors are divided. The relationship between the small sectors and the equivalent reference voltage vector is shown in Table 6.

[0150] Table 6 Small sector division

[0151]

[0152] Step S500: taking the corresponding optimal voltage vectors in the divided sectors as the preliminary optimal voltage vectors, and making a preliminary optimal voltage vector correspondence table for each sector.

[0153] Combination Figure 6 , the optimal voltage vectors corresponding to the small sectors in each large sector are shown in Table 7. Among them, in order to reduce the common mode voltage and switching frequency, the zero voltage vector only considers the "000" state, that is, V1.

[0154] Table 7 Correspondence table of optimal voltage vectors for each sector

[0155]

[0156]

[0157] Step S600: using the corresponding table of the prepared optimal voltage vectors of each sector, selecting the prepared optimal voltage vector corresponding to the reference voltage vector at time k.

[0158] For example, when the large sector of the reference voltage vector at time k is 1 and the small sector is I, the corresponding preliminary optimal voltage vector is V1. When the large sector of the reference voltage vector at time k is 1 and the small sector is II, the corresponding preliminary optimal voltage vectors are V4 and V5.

[0159] Step S700: determining an optimal voltage vector according to the prepared optimal voltage vector and the midpoint voltage.

[0160] It can be seen from Table 7 that when the small sector is II or III, there are two prepared optimal voltage vectors, which is due to the redundant characteristics of the small voltage vector. The two small voltage vectors at the same position have the same control effect on the grid current, however, the small voltage vectors at the same position have opposite effects on the midpoint voltage. Therefore, it is necessary to combine the midpoint voltage to select the final optimal voltage vector.

[0161] Combined with the dynamic mathematical model of the DC side midpoint voltage derived in step S200, the effect of the small voltage vector on the midpoint voltage is similar to the midpoint current i o Specifically, the midpoint current i corresponding to the small voltage vector o As shown in Table 8.

[0162] Table 8 Relationship between voltage vector and midpoint current

[0163] Small voltage vector <![CDATA[i o ]]> Small voltage vector <![CDATA[i o ]]> <![CDATA[V4]]> <![CDATA[-i a ]]> <![CDATA[V5]]> <![CDATA[i a ]]> <![CDATA[V6]]> <![CDATA[-i c ]]> <![CDATA[V7]]> <![CDATA[i c ]]> <![CDATA[V8]]> <![CDATA[-i b ]]> <![CDATA[V9]]> <![CDATA[i b ]]> <![CDATA[V 10 ]]> <![CDATA[-i a ]]> <![CDATA[V 11 ]]> <![CDATA[i a ]]> <![CDATA[V 12 ]]> <![CDATA[-i c ]]> <![CDATA[V 13 ]]> <![CDATA[i c ]]> <![CDATA[V 14 ]]> <![CDATA[-i b ]]> <![CDATA[V 15 ]]> <![CDATA[i b ]]>

[0164] When the prepared optimal voltage vector is a zero, medium, or large voltage vector, the prepared optimal voltage vector is unique, that is, the optimal voltage vector. When the prepared optimal voltage vector is a small voltage vector, it is necessary to select the optimal voltage vector from the prepared optimal voltage vectors in combination with the midpoint voltage.

[0165] The small voltage vector is divided into a P-type voltage vector and an N-type voltage vector. The P-type voltage vector can increase the midpoint voltage, and the N-type voltage vector can reduce the midpoint voltage. Combining the dynamic mathematical model of the DC side midpoint voltage derived in step S200, it can be seen that when i o When i is greater than zero, the voltage vector is an N-type voltage vector; when i o When the midpoint voltage is less than zero, the voltage vector is a P-type voltage vector. Therefore, when the midpoint voltage is greater than zero, an N-type voltage vector is selected; when the midpoint voltage is less than zero, a P-type voltage vector is selected.

[0166] Step S800: applying a gate drive signal corresponding to the optimal voltage vector to a power electronic semiconductor device of a three-level converter.

[0167] After a series of calculations in steps S100-S600, the control cycle value, comparison value and other information are transmitted to the FPGA through the communication module of the DSP, and the FPGA is used to implement the pulse distribution of the power device, the dead zone delay and other functions.

[0168] The following is an embodiment of the two-dimensional optimization model predictive control device for a three-level converter of the present invention, which can be used to execute the embodiment of the two-dimensional optimization model predictive control method for a three-level converter of the present invention. For details not disclosed in the embodiment of the two-dimensional optimization model predictive control device for a three-level converter of the present invention, please refer to the embodiment of the two-dimensional optimization model predictive control method for a three-level converter of the present invention.

[0169] In one embodiment, a two-dimensional optimization model predictive control device for a three-level converter is proposed, the device comprising:

[0170] A collection module, used to collect state variables of the T-type three-level energy storage converter at time k; the state variables include grid current, grid-side voltage and capacitor voltage;

[0171] The discrete mathematical model module is used to establish a discrete mathematical model of the T-type three-level energy storage converter in a two-phase static α-β coordinate system, and obtain the reference value of the grid current at time k+1 according to the Lagrange extrapolation theorem;

[0172] A calculation module, used to calculate the reference voltage vector and its phase angle of the inverter at time k by using the deadbeat principle;

[0173] A two-dimensional optimization voltage vector sector division module is used to divide the voltage vector region of the three-level converter into sectors based on the two-dimensional optimization principle;

[0174] A preparatory optimal voltage vector table module is used to use the optimal voltage vectors corresponding to the small and medium sectors of each large sector as preparatory optimal voltage vectors, and to prepare a corresponding table of preparatory optimal voltage vectors for each sector;

[0175] The module for determining the preliminary optimal voltage vector selects the preliminary optimal voltage vector corresponding to the reference voltage vector at time k by using the preliminary optimal voltage vector corresponding table of each sector;

[0176] An optimal voltage vector confirmation module is used to determine the optimal voltage vector according to the prepared optimal voltage vector and the midpoint voltage;

[0177] The action module is used to apply the optimal voltage vector and the gate drive signal corresponding to the action time thereof to the power electronic semiconductor device of the T-type three-level energy storage converter.

[0178] The various functional modules in the embodiments of the present invention may be integrated into one processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0179] As a specific example of the present invention, a three-level converter simulation model was built in MATLAB / Simulink to test and verify the present invention. The AC side of the three-level converter is connected to a 220V AC grid, and the DC side is powered by a battery with a voltage of 600V. The DC side capacitor is 2200μF and the filter inductor is 4mH. The sampling and control period is 100μs. In order to highlight the performance advantages of the present invention, the control performance of the traditional finite control set model predictive control and the two-dimensional optimization model predictive control proposed in the present invention are compared.

[0180] Figure 7 This is a comparison chart of steady-state simulation results of the traditional finite control set model predictive control and the two-dimensional optimization model predictive control when the grid reference current is 50A. The simulation waveform includes the three-phase grid current and the upper and lower bus capacitor voltages on the DC side. Figure 7 (a) is the steady-state waveform of the finite set model predictive control, Figure 7(b) is the steady-state waveform of the two-dimensional optimal model predictive control. It can be concluded from the simulation waveform that both control strategies can achieve stable control of the three-level converter. The current THD of the finite set model predictive control and the two-dimensional optimal model predictive control are 3.14% and 2.78% respectively, and the two-dimensional optimal model predictive control has better current quality. Both the finite set model predictive control and the two-dimensional optimal model predictive control can control the midpoint voltage within 5V, and both have good capacitor voltage control capabilities.

[0181] Figure 8 This is a comparison chart of the steady-state simulation results of the traditional finite control set model predictive control and the two-dimensional optimization model predictive control. The simulation waveform includes the a-phase grid current and the upper and lower bus capacitor voltages on the DC side. Among them, Figure 8 (a) is the dynamic waveform of the finite set model predictive control. Figure 8 (b) is the dynamic waveform of the two-dimensional optimization model predictive control. The two control strategies have similar dynamic performance. When the current reference value jumps from 25A to 50A, the dynamic response time of the two control strategies is about 2ms. When the current reference value jumps from 50A to 25A, the dynamic response time of the two control strategies is about 1ms. The simulation results show that no matter how the current changes, the output current can quickly track the current command changes, indicating that the present invention has a fast dynamic response. In the dynamic process, both control strategies have good capacitor-voltage balancing capabilities.

[0182] The above is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this patent can make some changes or modify the technical contents suggested above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the invention without departing from the content of the technical solution of the present invention still fall within the scope of the solution of the present invention.

Claims

1. A two-dimensional optimization model predictive control method for a three-level converter, characterized in that: The method comprises: Collecting state variables of the T-type three-level converter at time k; the state information includes grid current, grid voltage, and upper and lower bus capacitor voltages on the DC side; A discrete mathematical model of the three-level converter in a two-phase stationary α-β coordinate system is established, and based on the Lagrange extrapolation theorem, the reference value of the grid current at time k+1 in the α-β coordinate system is obtained; The reference voltage vector and its phase angle of the inverter at time k are calculated by the deadbeat principle; The voltage vector region of the three-level converter is divided into sectors based on the two-dimensional optimization principle; The corresponding voltage vectors in the divided sectors are used as the preliminary optimal voltage vectors, and a corresponding table of preliminary optimal voltage vectors for each sector is prepared; Using the corresponding table of the prepared optimal voltage vectors of each sector, the prepared optimal voltage vector corresponding to the reference voltage vector at time k is selected; Determine the optimal voltage vector according to the prepared optimal voltage vector and the midpoint voltage; The gate driving signal corresponding to the optimal voltage vector is applied to the power electronic semiconductor device of the three-level converter.

2. The two-dimensional optimization model predictive control method for a three-level converter according to claim 1, characterized in that: The discrete mathematical model of the T-type three-level energy storage converter in the two-phase static α-β coordinate system is: In the formula, L and R are the inductance value and equivalent resistance of the filter inductor respectively, T s is the sampling period; i α (k), i β (k) is the grid current sampling value in the α-β coordinate system at time k; i α (k+1), i β (k+1) is the predicted value of the grid current in the α-β coordinate system at time k+1; e α (k), e β (k) is the grid voltage sampling value in the α-β coordinate system at time k; u α (k),u β (k) is the AC side output voltage of the converter in the α-β coordinate system at time k, u o (k),u o (k+1) are the DC side midpoint voltage values ​​at time k and k+1 respectively; i o (k) is the midpoint current value of the DC side at time k; C1 and C2 are the capacitance values ​​of the upper and lower bus capacitors of the DC side respectively; T s is the control period of the control system.

3. The two-dimensional optimization model predictive control method for a three-level converter according to claim 2, characterized in that: According to the Lagrange extrapolation theorem, in the α-β coordinate system, the reference value of the grid current at time k+1 is: In the formula, are the reference values ​​of the α-axis grid current at the time k+1, k, k-1, and k-2 respectively, are the reference values ​​of the β-axis grid current at moments k+1, k, k-1, and k-2 respectively.

4. The two-dimensional optimization model predictive control method for a three-level converter according to claim 3, characterized in that: The deadbeat principle is to make the predicted value of the grid-side current at time k+1 equal to the grid-side current reference value obtained by Lagrange extrapolation theorem, and then calculate the reference voltage vector of the inverter at time k as: In the formula, are the components of the reference voltage on the α and β axes respectively; The phase angle θ of the reference voltage vector is:

5. The two-dimensional optimization model predictive control method for a three-level converter according to claim 4, characterized in that: The voltage vector region of the three-level converter is divided into sectors based on the two-dimensional optimization principle, including: The voltage vector region of the three-level converter is divided into six large sectors, each of which is further divided into six small sectors; the six large sectors are divided according to the magnitude of the reference voltage vector phase angle, and the small sectors are obtained by rotating the reference voltage vector to the first large sector to obtain an equivalent phase angle, and are divided according to the magnitude of the equivalent phase angle; In the α-β coordinate system, the reference voltage vector By positioning the voltage vector in two dimensions, the β-axis and the α-axis, the reference voltage vector can be expressed as: In the formula, u ref and θ ref are the amplitude and phase angle of the reference voltage respectively; The relationship between the 6 large sectors and the reference voltage vector phase angle is: The first largest sector corresponds to a phase angle of 0<θ ref ≤π / 3; The second largest sector corresponds to a phase angle of π / 3<θ ref ≤2π / 3; The phase angle corresponding to the third largest sector is 2π / 3<θ ref ≤π; The phase angle corresponding to the fourth largest sector is -π<θ ref ≤-2π / 3; The phase angle corresponding to the fifth largest sector is -2π / 3<θ ref ≤-π / 3; The phase angle corresponding to the sixth largest sector is -π / 3<θ ref ≤0.

6. The two-dimensional optimization model predictive control method for a three-level converter according to claim 5, characterized in that: In order to reduce the computational complexity, the reference voltage vector is rotated to the first largest sector, and the equivalent phase angle is: i refR =θ ref -(n-1)*π / 3 Where n is the large sector where the reference voltage is located; Equivalent reference voltage vector for: The relationship between the 6 small sectors and the equivalent reference voltage vector is: When the phase angle is limited to 0 ≤ θ refR ≤π / 6; Boundary restrictions Corresponding to the Ith small sector; Boundary restrictions and Corresponding to the IVth small sector; Boundary restrictions and Corresponding to the Vth small sector; Boundary restrictions and Corresponding to the second small sector; When the phase angle is limited to π / 6<θ refR ≤π / 3, Boundary restrictions Corresponding to the Ith small sector; Boundary restrictions and Corresponding to the VIth small sector; Boundary restrictions and Corresponding to the Vth small sector; Boundary restrictions and Corresponding to the third small sector.

7. The two-dimensional optimization model predictive control method for a three-level converter according to claim 6, characterized in that: The zero voltage vector only considers the "000" state, that is, V1. The optimal voltage vector corresponding to the small sector in each large sector is: In the first large sector, the optimal voltage vector of the first small sector is V1, the optimal voltage vector of the second small sector is V4 and V5, the optimal voltage vector of the third small sector is V6 and V7, and the optimal voltage vector of the fourth small sector is V 22 、The optimal voltage vector of the Vth small sector is V 16 、The optimal voltage vector of the VIth small sector is V 23 ; In the second largest sector, the optimal voltage vector of the first small sector is V1, the optimal voltage vector of the second small sector is V6 and V7, the optimal voltage vector of the third small sector is V8 and V9, and the optimal voltage vector of the fourth small sector is V 23 、The optimal voltage vector of the Vth small sector is V 17 、The optimal voltage vector of the VIth small sector is V 24 ; In the third largest sector, the optimal voltage vector of the first small sector is V1, the optimal voltage vector of the second small sector is V8 and V9, and the optimal voltage vector of the third small sector is V 10 and V 11 , the optimal voltage vector of the IVth small sector is V 24 、The optimal voltage vector of the Vth small sector is V 18 、The optimal voltage vector of the VIth small sector is V 25 ; In the fourth largest sector, the optimal voltage vector of the first small sector is V1, and the optimal voltage vector of the second small sector is V 10 and V 11 , the optimal voltage vector of the third small sector is V 12 and V 13 , the optimal voltage vector of the IVth small sector is V 25 、The optimal voltage vector of the Vth small sector is V 19 、The optimal voltage vector of the VIth small sector is V 26 ; In the fifth largest sector, the optimal voltage vector of the first small sector is V1, and the optimal voltage vector of the second small sector is V 12 and V 13 , the optimal voltage vector of the third small sector is V 14 and V 15 , the optimal voltage vector of the IVth small sector is V 26 、The optimal voltage vector of the Vth small sector is V 20 、The optimal voltage vector of the VIth small sector is V 27 ; In the sixth largest sector, the optimal voltage vector of the first small sector is V1, and the optimal voltage vector of the second small sector is V 14 and V 15 The optimal voltage vectors for the third small sector are V4 and V5, and the optimal voltage vector for the fourth small sector is V 27 、The optimal voltage vector of the Vth small sector is V 21 、The optimal voltage vector of the VIth small sector is V 22 .

8. The two-dimensional optimization model predictive control method for a three-level converter according to claim 1, characterized in that: The step of determining the optimal voltage vector according to the prepared optimal voltage vector and the midpoint voltage includes: When the prepared optimal voltage vector is a zero, medium, or large voltage vector, the prepared optimal voltage vector is the optimal voltage vector; When the prepared optimal voltage vector is a small voltage vector, the optimal voltage vector is selected from the prepared optimal voltage vectors in combination with the midpoint voltage.

9. The two-dimensional optimization model predictive control method for a three-level converter according to claim 8, characterized in that: The small voltage vector is divided into a P-type voltage vector and an N-type voltage vector; the P-type voltage vector can increase the midpoint voltage, and the N-type voltage vector can reduce the midpoint voltage; The midpoint voltage is obtained by combining the dynamic mathematical model of the DC side midpoint voltage; when the midpoint voltage is greater than zero, an N-type voltage vector is selected; when the midpoint voltage is less than zero, a P-type voltage vector is selected.

10. A two-dimensional optimization model predictive control device for a three-level converter, characterized in that: The device comprises: A collection module, used to collect state variables of the T-type three-level energy storage converter at time k; the state variables include grid current, grid-side voltage and capacitor voltage; The discrete mathematical model module is used to establish a discrete mathematical model of the T-type three-level energy storage converter in a two-phase static α-β coordinate system, and obtain the reference value of the grid current at time k+1 according to the Lagrange extrapolation theorem; A calculation module, used to calculate the reference voltage vector and its phase angle of the inverter at time k by using the deadbeat principle; A two-dimensional optimization voltage vector sector division module is used to divide the voltage vector region of the three-level converter into sectors based on the two-dimensional optimization principle; A preparatory optimal voltage vector table module is used to use the optimal voltage vectors corresponding to the small and medium sectors of each large sector as preparatory optimal voltage vectors, and to prepare a corresponding table of preparatory optimal voltage vectors for each sector; The module for determining the preliminary optimal voltage vector selects the preliminary optimal voltage vector corresponding to the reference voltage vector at time k by using the preliminary optimal voltage vector corresponding table of each sector; An optimal voltage vector confirmation module is used to determine the optimal voltage vector according to the prepared optimal voltage vector and the midpoint voltage; The action module is used to apply the optimal voltage vector and the gate drive signal corresponding to the action time thereof to the power electronic semiconductor device of the T-type three-level energy storage converter.

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