T-type three-level energy storage converter hybrid model prediction control method and device

Abstract

The invention discloses a hybrid model predictive control method and device for a three-level energy storage converter, and the method comprises the steps: determining a candidate voltage vector based on a voltage vector conversion rule; calculating a cost function value of each candidate voltage vector through a cost function, and selecting the voltage vector corresponding to the minimum cost function value as a preparatory optimal voltage vector of the kth control period; determining an optimal voltage vector of the kth control period according to the combination of the preparation optimal voltage vector and the neutral-point voltage of the kth control period; in the kth control period, the duty ratio of the optimal voltage vector in the (k-1) th and kth periods is calculated; and applying the gate driving signal corresponding to the optimal voltage vector and the action time of the optimal voltage vector to a power electronic semiconductor device of the T-type three-level energy storage converter. The advantages that finite set model prediction control is visual, simple and convenient and continuous set model prediction control is high in current quality are combined, and current ripple attenuation and current quality optimization of the T-type three-level energy storage converter can be achieved.

Description

Technical Field

[0001] The present invention belongs to the field of three-level inverter control, and in particular relates to a hybrid model predictive control method and device for a T-type three-level energy storage 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 adjustment, and the ability to handle multivariable constraints and implement multivariable control in a single control loop. Model predictive control can be divided into finite set model predictive control and continuous set model predictive control. Finite set model predictive control does not require a modulation link, and has the advantages of simple calculation and easy implementation. However, the switching frequency is not fixed and the output current ripple is large. Continuous set model predictive control has a fixed switching frequency and higher current quality. However, an additional modulation link needs to be added, and the amount of calculation is large.

[0004] In order to solve the above problems, there is an urgent need for a new hybrid model predictive control method that has the advantages of finite set model predictive control and continuous set model predictive control. Summary of the invention

[0005] The purpose of the present invention is to provide a hybrid model predictive control method and device for a T-type three-level energy storage converter. It is used to solve the problems of high algorithm complexity and poor current quality in traditional methods. The present invention combines the advantages of finite set model predictive control, which is intuitive and simple, easy to implement, and continuous set model predictive control, which has high current quality, and can achieve current ripple attenuation and current quality optimization of a T-type three-level energy storage converter.

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

[0007] The present invention provides a hybrid model predictive control method for a T-type three-level energy storage converter, the method comprising:

[0008] Collecting 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;

[0009] A discrete mathematical model of a T-type three-level energy storage converter in a two-phase stationary α-β coordinate system is established, and the reference value of the grid current at time k+1 is obtained according to the Lagrange extrapolation theorem.

[0010] Based on the voltage vector conversion rule, a candidate voltage vector is determined;

[0011] Calculate the cost function value of each candidate voltage vector through the cost function, and select the voltage vector corresponding to the minimum cost function value as the preliminary optimal voltage vector of the kth control cycle;

[0012] Determine the optimal voltage vector of the kth control cycle according to the combination of the prepared optimal voltage vector and the midpoint voltage of the kth control cycle;

[0013] In the kth control cycle, the duty cycle of the optimal voltage vector of the k-1th and kth cycles is calculated;

[0014] The optimal voltage vector and the gate driving signal corresponding to the action time thereof are applied to the power electronic semiconductor device of the T-type three-level energy storage converter.

[0015] 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:

[0016]

[0017] 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.

[0018] In one implementation, the reference value of the grid current at time k+1 calculated according to the Lagrange extrapolation theorem is:

[0019]

[0020] 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.

[0021] In one implementation, the voltage vector conversion rule is:

[0022] Only the voltage vector adjacent to the optimal voltage vector at the previous moment is selected, and the change of the phase voltage and line voltage of the three-level energy storage converter cannot exceed u dc The voltage vector required by the voltage change of / 2 is taken as the candidate voltage vector, and the zero voltage vector only considers the "000" state.

[0023] In one implementation, the candidate voltage vector is determined based on the voltage vector conversion rule as:

[0024] The optimal voltage vector for the k-1 cycle is: V1, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V4, V5, V6, V7, V8, V9, V 10 、V 11 、V 12 、V 13 、V 14 、V 15 ;

[0025] The optimal voltage vector for the k-1 cycle is: V2, and the corresponding candidate voltage vector for the k cycle is: None;

[0026] The optimal voltage vector for the k-1 cycle is: V3, and the corresponding candidate voltage vector for the k cycle is: None;

[0027] The optimal voltage vector for the k-1 cycle is: V4, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V4, V6, V7, V 14 、V 15 、V 16 、V 21 、V 22 ;

[0028] The optimal voltage vector for the k-1 cycle is: V5, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V5, V6, V 14 、V 16 、V 21 、V 22 ;

[0029] The optimal voltage vector for the k-1 cycle is: V6, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V4, V5, V6, V8, V9, V16 、V 17 、V 23 ;

[0030] The optimal voltage vector for the k-1 cycle is: V7, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V4, V7, V8, V 16 、V 17 、V 23 ;

[0031] The optimal voltage vector for the k-1 cycle is: V8, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V6, V7, V8, V 10 、V 11 、V 17 、V 18 、V 24 ;

[0032] The optimal voltage vector for the k-1 cycle is: V9, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V6, V9, V 10 、V 17 、V 18 、V 24 ;

[0033] The optimal voltage vector for k-1 period is: V 10 , the corresponding k-cycle candidate voltage vectors are: V1, V8, V9, V 10 、V 12 、V 13 、V 18 、V 19 、V 25 ;

[0034] The optimal voltage vector for k-1 period is: V 11 , the corresponding k-cycle candidate voltage vectors are: V1, V8, V 11 、V 12 、V 18 、V 19 、V 25 ;

[0035] The optimal voltage vector for k-1 period is: V 12 , the corresponding k-cycle candidate voltage vectors are: V1, V 10 、V 11 、V 12 、V 14 、V 15 、V 19 、V 20 、V 26 ;

[0036] The optimal voltage vector for k-1 period is: V 13 , the corresponding k-cycle candidate voltage vectors are: V1, V10 、V 13 、V 14 、V 19 、V 20 、V 26 ;

[0037] The optimal voltage vector for k-1 period is: V 14 , the corresponding k-cycle candidate voltage vectors are: V1, V4, V5, V 12 、V 13 、V 14 、V 20 、V 21 、V 27 ;

[0038] The optimal voltage vector for k-1 period is: V 15 , the corresponding k-cycle candidate voltage vectors are: V1, V4, V 12 、V 15 、V 20 、V 21 、V 27 ;

[0039] The optimal voltage vector for k-1 period is: V 16 , the corresponding k-cycle candidate voltage vectors are: V4, V5, V6, V7, V 16 、V 22 、V 23 ;

[0040] The optimal voltage vector for k-1 period is: V 17 , the corresponding k-cycle candidate voltage vectors are: V6, V7, V8, V9, V 17 、V 23 、V 24 ;

[0041] The optimal voltage vector for k-1 period is: V 18 , the corresponding k-cycle candidate voltage vectors are: V8, V9, V 10 、V 11 、V 18 、V 24 、V 25 ;

[0042] The optimal voltage vector for k-1 period is: V 19 , the corresponding k-cycle candidate voltage vector is: V 10 、V 11 、V 12 、V 13 、V 19 、V 25 、V 26 ;

[0043] The optimal voltage vector for k-1 period is: V 20 , the corresponding k-cycle candidate voltage vector is: V 12 、V 13 、V 14 、V 15 、V 20 、V 26 、V 27 ;

[0044] The optimal voltage vector for k-1 period is: V 21 , the corresponding k-cycle candidate voltage vectors are: V4, V5, V 14 、V 15 、V 21 、V 22 、V 27 ;

[0045] The optimal voltage vector for k-1 period is: V 22 , the corresponding k-cycle candidate voltage vectors are: V4, V5, V 16 、V 21 、V 22 ;

[0046] The optimal voltage vector for k-1 period is: V 23 , the corresponding k-cycle candidate voltage vectors are: V6, V7, V 16 、V 17 、V 23 ;

[0047] The optimal voltage vector for k-1 period is: V 24 , the corresponding k-cycle candidate voltage vectors are: V8, V9, V 17 、V 18 、V 24 ;

[0048] The optimal voltage vector for k-1 period is: V 25 , the corresponding k-cycle candidate voltage vector is: V 10 、V 11 、V 18 、V 19 、V 25 ;

[0049] The optimal voltage vector for k-1 period is: V 26 , the corresponding k-cycle candidate voltage vector is: V 12 、V 13 、V 19 、V 20 、V 26 ;

[0050] The optimal voltage vector for k-1 period is: V 27 , the corresponding k-cycle candidate voltage vector is: V13 、V 14 、V 20 、V 21 、V 27 .

[0051] In one implementation, the cost function only includes the grid current control target, specifically:

[0052]

[0053] in, is the grid current reference value in the α-β coordinate system at time k+1, 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.

[0054] In one embodiment, 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, it is necessary to select the optimal voltage vector from the prepared optimal voltage vector in combination with the midpoint voltage;

[0055] Determining the optimal voltage vector according to the relationship between the small voltage vector and the midpoint current includes:

[0056] The small voltage vector is divided into a P-type voltage vector and an N-type voltage vector, wherein the P-type voltage vector is a small voltage vector corresponding to a midpoint current less than zero, and the N-type voltage vector is a small voltage vector corresponding to a midpoint current greater than zero;

[0057] When the midpoint voltage is greater than zero, the N-type voltage vector is selected as the optimal voltage vector; when the midpoint voltage is less than zero, the P-type voltage vector is selected as the optimal voltage vector.

[0058] In one implementation, the duty cycle of the voltage vector is:

[0059]

[0060] Among them, J1 and J2 are the cost function values ​​of the optimal voltage vectors of k-1 and k cycles respectively, and d1 and d2 are the duty cycles of the optimal voltage vectors of k-1 and k cycles respectively.

[0061] The present invention also provides a T-type three-level energy storage converter hybrid model predictive control device, the device comprising:

[0062] 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;

[0063] 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;

[0064] A candidate voltage vector selection module, used for determining a candidate voltage vector based on a voltage vector conversion rule;

[0065] A preliminary voltage vector selection module, used to calculate the cost function value of each candidate voltage vector through a cost function, and select the voltage vector corresponding to the minimum cost function value as the preliminary optimal voltage vector of the kth control cycle;

[0066] An optimal voltage vector determination module, used to determine the optimal voltage vector of the kth control cycle according to the combination of the prepared optimal voltage vector and the midpoint voltage of the kth control cycle;

[0067] A voltage vector duty cycle calculation module is used to calculate the duty cycle of the optimal voltage vector in the k-1th and kth cycles in the kth control cycle;

[0068] 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.

[0069] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0070] 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

[0071] 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.

[0072] Figure 1 A topological diagram of a T-type three-level energy storage converter provided in an embodiment of the present invention;

[0073] Figure 2 A voltage vector distribution diagram of a T-type three-level energy storage converter provided in an embodiment of the present invention;

[0074] Figure 3 A flow chart of a hybrid model predictive control method for a T-type three-level energy storage converter provided by an embodiment of the present invention;

[0075] Figure 4 A voltage vector switching rule diagram of a T-type three-level energy storage converter provided in an embodiment of the present invention;

[0076] Figure 5 This is a comparison diagram of the optimal voltage vector used in the finite set model predictive control and the hybrid model predictive control provided in the embodiment of the present invention, wherein: Figure 5 (a) The traditional finite set model predictive control uses the optimal voltage vector at the start of the control cycle. Figure 5 (b) the optimal voltage vector for the hybrid model predictive control proposed in the present invention;

[0077] Figure 6 It is a comparison diagram of steady-state simulation waveforms of the finite set model predictive control and the hybrid model predictive control provided in the embodiment of the present invention, wherein: Figure 6 (a) is the steady-state waveform of the finite set model predictive control, Figure 6 (b) is the steady-state waveform of the hybrid model predictive control;

[0078] Figure 7 It is a comparison diagram of dynamic simulation waveforms of finite set model predictive control and hybrid model predictive control provided in the embodiment of the present invention, wherein: Figure 7 (a) is the dynamic waveform of the finite set model predictive control. Figure 7 (b) is the dynamic waveform of hybrid model predictive control. DETAILED DESCRIPTION

[0079] 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.

[0080] 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.

[0081] Figure 1 The 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 xare 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.

[0082] Table 1 Relationship between output voltage, switch state and gate signal, x = (a, b, c)

[0083]

[0084] 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.

[0085] According to Kirchhoff's voltage law, in the three-phase stationary coordinate system, the dynamic response of the grid current is:

[0086]

[0087] 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.

[0088] Table 2 Voltage vector table

[0089]

[0090] like Figure 3As 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:

[0091] Step S100: collecting state variables of a T-type three-level energy storage converter at time k.

[0092] 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).

[0093] 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.

[0094] The three-phase stationary coordinate system can be converted into a two-phase stationary coordinate system through Clarke transformation. Clarke transformation is:

[0095]

[0096] The mathematical model of the three-level energy storage converter in the two-phase stationary coordinate system is:

[0097]

[0098] 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:

[0099]

[0100] 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.

[0101]

[0102] The DC side midpoint voltage is:

[0103] u o =u c2 -u c1

[0104] 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:

[0105]

[0106] i o is the DC side midpoint current, which is given by the following formula:

[0107] i o =|S abc |i abc

[0108] In the formula, |S abc |=[1-|S a |,1-|S b |,1-|S c |],i abc =[i a ,i b ,i c ].

[0109] The predicted value of the midpoint voltage is:

[0110]

[0111] 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.

[0112] According to Lagrange's extrapolation theorem, in the α-β coordinate system, the reference value of the grid current at time k+1 is:

[0113]

[0114] 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.

[0115] Step S300: Determine a candidate voltage vector based on a voltage vector conversion rule.

[0116] In the application embodiment, the voltage vector switching rules are formulated by comprehensively considering the characteristics of the three-level energy storage converter itself, reducing the damage to power electronic semiconductor devices and the impact on the power grid, and reducing the complexity and calculation amount of the algorithm.

[0117] The change of phase voltage and line voltage of three-level energy storage converter cannot exceed u dc / 2, because the former may damage the switching devices of the converter, and the latter will have a greater impact on the power grid. Therefore, for the three-level energy storage converter, not all voltage vectors can be directly converted. In addition, in order to reduce the amount of calculation of the algorithm, the candidate voltage vectors of each cycle should be optimized under the premise of meeting the above requirements.

[0118] The voltage vector switching rule in the present invention is as follows: only the voltage vectors that are adjacent to the optimal voltage vector at the previous moment and meet the voltage change requirements in the above analysis are selected as candidate voltage vectors. Figure 4 As shown, in order to reduce the common mode voltage and switching frequency, the zero voltage vector only considers the "000" state.

[0119] Specifically, the candidate voltage vectors corresponding to each voltage vector are shown in Table 3.

[0120] Table 3 Candidate voltage vector switching rules

[0121]

[0122]

[0123] Step S400: Calculate the cost function value of each candidate voltage vector through the cost function, and select the voltage vector corresponding to the minimum cost function value as the preliminary optimal voltage vector of the kth control cycle.

[0124] Specifically, according to Figure 4 The candidate voltage vectors determined in Table 3 are combined with the cost function to calculate the cost function value of each candidate voltage vector.

[0125] The cost function of a three-level energy storage converter usually includes the grid current and the midpoint voltage. The cost function is:

[0126]

[0127] where k uis a weight factor used to adjust the control priority of the grid current and midpoint voltage. However, the above weight factors have the problem of large amount of calculation and cumbersome adjustment of weight factors. Since the small voltage vectors at the same position have the same current control ability and opposite midpoint voltage control effect, they can be used to adjust the midpoint voltage.

[0128] The improved cost function only contains the grid current control objective:

[0129]

[0130] Based on the above cost function, the cost function values ​​of all candidate voltage vectors can be calculated. The voltage vector corresponding to the minimum cost function value is selected as the preliminary minimum voltage vector.

[0131] Step S500: Determine the optimal voltage vector of the kth control cycle according to the combination of the prepared optimal voltage vector and the midpoint voltage of the kth control cycle;

[0132] If the calculated preliminary optimal voltage vector is a small voltage vector, due to the redundancy of the small voltage vector, the cost function values ​​of the two small voltage vectors at the same position are the same, so there are two preliminary optimal voltage vectors. However, the small voltage vectors at the same position have opposite effects on the midpoint voltage. Therefore, it is necessary to further determine the optimal voltage vector in combination with the midpoint voltage. When the preliminary optimal voltage vector is a zero, medium, or large voltage vector, they are unique because they are not redundant. Therefore, the preliminary optimal voltage vector is the optimal voltage vector.

[0133] Combined with the dynamic mathematical model of the DC side midpoint voltage derived in step S200, the effect of the small 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 4.

[0134] Table 4 Relationship between voltage vector and midpoint current

[0135] 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 ]]>

[0136] The small voltage vector can be 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. 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.

[0137] Based on the above analysis, 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, it is necessary to select the optimal voltage vector from the prepared optimal voltage vectors in combination with the midpoint voltage.

[0138] Step S600: In the kth control cycle, the duty ratios of the optimal voltage vectors in the k-1th and kth cycles are calculated.

[0139] Assume that the optimal voltage vectors for cycles k-1 and k are V4 and V7 respectively. The traditional finite set model predictive control uses the optimal voltage vector at the start of the control cycle, such as Figure 5 (a). The voltage vector remains unchanged throughout the control cycle. Therefore, the finite set model predictive control has a discrete output characteristic.

[0140] The hybrid model predictive control proposed in the present invention still applies the optimal voltage vector of the previous control cycle at the start of the control cycle, and the optimal voltage vector of the current cycle is switched at a specific time, such as Figure 5 (b) In one control cycle, the action time of the two voltage vectors can be adjusted. Therefore, the hybrid model predictive control can obtain a time-continuous output with better current ripple suppression effect and current quality.

[0141] Furthermore, the duty cycle of the voltage vector is inversely proportional to the second power of the cost function value, and the duty cycle of the voltage vector is:

[0142]

[0143] J1 and J2 are the cost function values ​​of the optimal voltage vectors for k-1 and k cycles respectively. d1 and d2 are the duty cycles of the optimal voltage vectors for k-1 and k cycles respectively.

[0144] Step S700: applying the optimal voltage vector and the gate driving signal corresponding to the action time thereof to the power electronic semiconductor device of the T-type three-level energy storage converter.

[0145] 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.

[0146] The following is an embodiment of the hybrid model predictive control device of the three-level energy storage converter of the present invention, which can be used to execute the embodiment of the hybrid model predictive control method of the three-level energy storage converter of the present invention. For details not disclosed in the embodiment of the hybrid model predictive control device of the three-level energy storage converter of the present invention, please refer to the embodiment of the hybrid model predictive control method of the three-level energy storage converter of the present invention.

[0147] In one embodiment, a hybrid model predictive control device for a three-level energy storage converter is proposed, the device comprising:

[0148] 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;

[0149] 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;

[0150] A candidate voltage vector selection module, used for determining a candidate voltage vector based on a voltage vector conversion rule;

[0151] A preliminary voltage vector selection module, used to calculate the cost function value of each candidate voltage vector through a cost function, and select the voltage vector corresponding to the minimum cost function value as the preliminary optimal voltage vector of the kth control cycle;

[0152] An optimal voltage vector determination module, used to determine the optimal voltage vector of the kth control cycle according to the combination of the prepared optimal voltage vector and the midpoint voltage of the kth control cycle;

[0153] A voltage vector duty cycle calculation module is used to calculate the duty cycle of the optimal voltage vector in the k-1th and kth cycles in the kth control cycle;

[0154] 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.

[0155] 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.

[0156] As a specific example of the present invention, a three-level energy storage converter simulation model was built in MATLAB / Simulink to test and verify the present invention. The AC side of the three-level energy storage 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 advantages of the present invention in improving current quality, the control performance of the traditional finite control set model predictive control and the hybrid model predictive control proposed in the present invention are compared.

[0157] Figure 6The following is a comparison of the steady-state simulation results of the traditional finite control set model predictive control and the hybrid 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 6 (a) is the steady-state waveform of the finite set model predictive control, Figure 6 (b) is the steady-state waveform of the hybrid model predictive control. It can be concluded from the simulation waveform that both control strategies can achieve stable control of the three-level energy storage converter. The current THD of the finite set model predictive control and the hybrid model predictive control are 3.14% and 2.56% respectively, and the hybrid model predictive control has better current quality. Both the finite set model predictive control and the hybrid model predictive control can control the midpoint voltage within 5V, and both have good capacitor voltage control capabilities.

[0158] Figure 7 The figure is a comparison of the steady-state simulation results of the traditional finite control set model predictive control and the hybrid model predictive control. The simulation waveform includes the a-phase grid current and the upper and lower bus capacitor voltages on the DC side. Figure 7 (a) is the dynamic waveform of the finite set model predictive control. Figure 7 (b) is the dynamic waveform of the hybrid 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.

[0159] 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 hybrid model predictive control method for a T-type three-level energy storage converter, characterized in that: The method comprises: Collecting 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; A discrete mathematical model of a T-type three-level energy storage converter in a two-phase stationary α-β coordinate system is established, and the reference value of the grid current at time k+1 is obtained according to the Lagrange extrapolation theorem. Based on the voltage vector conversion rule, a candidate voltage vector is determined; Calculate the cost function value of each candidate voltage vector through the cost function, and select the voltage vector corresponding to the minimum cost function value as the preliminary optimal voltage vector of the kth control cycle; Determine the optimal voltage vector of the kth control cycle according to the combination of the prepared optimal voltage vector and the midpoint voltage of the kth control cycle; In the kth control cycle, the duty cycle of the optimal voltage vector of the k-1th and kth cycles is calculated; The optimal voltage vector and the gate driving signal corresponding to the action time thereof are applied to the power electronic semiconductor device of the T-type three-level energy storage converter.

2. The hybrid model predictive control method for a T-type three-level energy storage 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 hybrid model predictive control method for a T-type three-level energy storage 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 hybrid model predictive control method for a T-type three-level energy storage converter according to claim 1, characterized in that: The voltage vector conversion rule is: Only the voltage vector adjacent to the optimal voltage vector at the previous moment is selected, and the change of the phase voltage and line voltage of the three-level energy storage converter cannot exceed u dc The voltage vector required by the voltage change of / 2 is taken as the candidate voltage vector, and the zero voltage vector only considers the "000" state.

5. The hybrid model predictive control method for a T-type three-level energy storage converter according to claim 4, characterized in that: Based on the voltage vector conversion rule, the candidate voltage vector is determined as: The optimal voltage vector for the k-1 cycle is: V1, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V4, V5, V6, V7, V8, V9, V 10 、V 11 、V 12 、V 13 、V 14 、V 15 ; The optimal voltage vector for the k-1 cycle is: V2, and the corresponding candidate voltage vector for the k cycle is: None; The optimal voltage vector for the k-1 cycle is: V3, and the corresponding candidate voltage vector for the k cycle is: None; The optimal voltage vector for the k-1 cycle is: V4, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V4, V6, V7, V 14 、V 15 、V 16 、V 21 、V 22 ; The optimal voltage vector for the k-1 cycle is: V5, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V5, V6, V 14 、V 16 、V 21 、V 22 ; The optimal voltage vector for the k-1 cycle is: V6, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V4, V5, V6, V8, V9, V 16 、V 17 、V 23 ; The optimal voltage vector for the k-1 cycle is: V7, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V4, V7, V8, V 16 、V 17 、V 23 ; The optimal voltage vector for the k-1 cycle is: V8, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V6, V7, V8, V 10 、V 11 、V 17 、V 18 、V 24 ; The optimal voltage vector for the k-1 cycle is: V9, and the corresponding candidate voltage vectors for the k-1 cycle are: V1, V6, V9, V 10 、V 17 、V 18 、V 24 ; The optimal voltage vector for k-1 period is: V 10 , the corresponding k-cycle candidate voltage vectors are: V1, V8, V9, V 10 、V 12 、V 13 、V 18 、V 19 、V 25 ; The optimal voltage vector for k-1 period is: V 11 , the corresponding k-cycle candidate voltage vectors are: V1, V8, V 11 、V 12 、V 18 、V 19 、V 25 ; The optimal voltage vector for k-1 period is: V 12 , the corresponding k-cycle candidate voltage vectors are: V1, V 10 、V 11 、V 12 、V 14 、V 15 、V 19 、V 20 、V 26 ; The optimal voltage vector for k-1 period is: V 13 , the corresponding k-cycle candidate voltage vectors are: V1, V 10 、V 13 、V 14 、V 19 、V 20 、V 26 ; The optimal voltage vector for k-1 period is: V 14 , the corresponding k-cycle candidate voltage vectors are: V1, V4, V5, V 12 、V 13 、V 14 、V 20 、V 21 、V 27 ; The optimal voltage vector for k-1 period is: V 15 , the corresponding k-cycle candidate voltage vectors are: V1, V4, V 12 、V 15 、V 20 、V 21 、V 27 ; The optimal voltage vector for k-1 period is: V 16 , the corresponding k-cycle candidate voltage vectors are: V4, V5, V6, V7, V 16 、V 22 、V 23 ; The optimal voltage vector for k-1 period is: V 17 , the corresponding k-cycle candidate voltage vectors are: V6, V7, V8, V9, V 17 、V 23 、V 24 ; The optimal voltage vector for k-1 period is: V 18 , the corresponding k-cycle candidate voltage vectors are: V8, V9, V 10 、V 11 、V 18 、V 24 、V 25 ; The optimal voltage vector for k-1 period is: V 19 , the corresponding k-cycle candidate voltage vector is: V 10 、V 11 、V 12 、V 13 、V 19 、V 25 、V 26 ; The optimal voltage vector for k-1 period is: V 20 , the corresponding k-cycle candidate voltage vector is: V 12 、V 13 、V 14 、V 15 、V 20 、V 26 、V 27 ; The optimal voltage vector for k-1 period is: V 21 , the corresponding k-cycle candidate voltage vectors are: V4, V5, V 14 、V 15 、V 21 、V 22 、V 27 ; The optimal voltage vector for k-1 period is: V 22 , the corresponding k-cycle candidate voltage vectors are: V4, V5, V 16 、V 21 、V 22 ; The optimal voltage vector for k-1 period is: V 23 , the corresponding k-cycle candidate voltage vectors are: V6, V7, V 16 、V 17 、V 23 ; The optimal voltage vector for k-1 period is: V 24 , the corresponding k-cycle candidate voltage vectors are: V8, V9, V 17 、V 18 、V 24 ; The optimal voltage vector for k-1 period is: V 25 , the corresponding k-cycle candidate voltage vector is: V 10 、V 11 、V 18 、V 19 、V 25 ; The optimal voltage vector for k-1 period is: V 26 , the corresponding k-cycle candidate voltage vector is: V 12 、V 13 、V 19 、V 20 、V 26 ; The optimal voltage vector for k-1 period is: V 27 , the corresponding k-cycle candidate voltage vector is: V 13 、V 14 、V 20 、V 21 、V 27 .

6. The hybrid model predictive control method for a T-type three-level energy storage converter according to claim 1 or 5, characterized in that: The cost function only includes the grid current control target, specifically: in, is the grid current reference value in the α-β coordinate system at time k+1, 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.

7. The hybrid model predictive control method for a T-type three-level energy storage converter according to claim 1, characterized in that: 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, it is necessary to select the optimal voltage vector from the prepared optimal voltage vector in combination with the midpoint voltage; The step of determining the optimal voltage vector according to the relationship between the small voltage vector and the midpoint current includes: The small voltage vector is divided into a P-type voltage vector and an N-type voltage vector, wherein the P-type voltage vector is a small voltage vector corresponding to a midpoint current less than zero, and the N-type voltage vector is a small voltage vector corresponding to a midpoint current greater than zero; When the midpoint voltage is greater than zero, the N-type voltage vector is selected as the optimal voltage vector; when the midpoint voltage is less than zero, the P-type voltage vector is selected as the optimal voltage vector.

8. The hybrid model predictive control method for a T-type three-level energy storage converter according to claim 1, wherein the duty cycle of the voltage vector is: in, J1 and J2 are the cost function values ​​of the optimal voltage vectors for k-1 and k cycles respectively, and d1 and d2 are the duty cycles of the optimal voltage vectors for k-1 and k cycles respectively.

9. A hybrid model predictive control device for a T-type three-level energy storage 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 candidate voltage vector selection module, used for determining a candidate voltage vector based on a voltage vector conversion rule; A preliminary voltage vector selection module, used to calculate the cost function value of each candidate voltage vector through a cost function, and select the voltage vector corresponding to the minimum cost function value as the preliminary optimal voltage vector of the kth control cycle; An optimal voltage vector determination module, used to determine the optimal voltage vector of the kth control cycle according to the combination of the prepared optimal voltage vector and the midpoint voltage of the kth control cycle; A voltage vector duty cycle calculation module is used to calculate the duty cycle of the optimal voltage vector in the k-1th and kth cycles in the kth control cycle; 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.

Images