Weight-factor-free hybrid model predictive control method and device for network construction type converter

Abstract

The invention discloses a weight factor-free hybrid model prediction control method and device for a network construction type converter, and the method comprises the steps: building cost functions of an output voltage vector of the converter, and the cost functions comprise a first cost function, a second cost function and a third cost function; calculating a first output value of the first cost function, and selecting the first output value and a corresponding output voltage vector set from the first output value; and substituting the output voltage vector set into the second cost function and the third cost function, calculating a second output value and a third output value, and taking the output voltage vector corresponding to the optimal second output value or the optimal third output value as the optimal output voltage vector. By the adoption of the technical scheme, the original cost function is divided into the multiple cost functions, the cost functions are calculated respectively, the optimal parameters capable of comprehensively meeting optimization of all the cost functions are screened, and the negative influence of the weight in the cost functions is effectively avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of power electronics, and in particular to a hybrid model predictive control method and device for a grid-connected converter without weight factors. Background Art

[0002] In new power systems, the proportion of power electronic equipment represented by voltage source converters has increased dramatically, while the proportion of traditional small synchronous generator sets has gradually decreased. This change is constantly changing the form of modern power systems, resulting in a decrease in the system's inertial support and primary and secondary frequency regulation capabilities, and stability has been affected to a certain extent. As an important device for AC and DC energy transmission, power electronic equipment has fast response and convenient control through traditional control technology, but it cannot show inertia and damping characteristics to the outside world. For this reason, a grid-type converter control that simulates the characteristics of synchronous generators using the rotor motion equation that links power and frequency has emerged, namely the Virtual Synchronous Generator (VSG) solution.

[0003] In the prior art, the control scheme for virtual synchronous generators mainly includes a VSG control scheme based on model predictive control, which samples the output current of the converter, generates reference current and predicted current respectively through the virtual synchronous generator and the current prediction model, and establishes a prediction model with the frequency change rate as a constraint and the frequency deviation and the output of the virtual synchronous generator as the optimization target. The problem with the above model predictive control scheme is that the weights of various factors in the corresponding cost function or target optimization function need to be considered, which is complicated in calculation and has the problem of unreasonable weights. Summary of the invention

[0004] Purpose of the invention: The present invention provides a hybrid model predictive control method and device for a grid-type converter without weight factors, aiming to solve the problem in the prior art that in the VSG control scheme based on model predictive control, the weight of each factor in the cost function needs to be considered.

[0005] Technical solution: The present invention provides a hybrid model predictive control method for a grid-type converter without weight factors, including: controlling the converter to simulate the working mode of a synchronous generator and providing the output three-phase AC current to the power grid; sampling the output current, output voltage vector and power grid voltage of the converter, converting the current and voltage into an αβ coordinate system and discretizing them at the same time, and establishing a current prediction function for predicting the output current of the converter at the next moment; establishing a cost function for the output voltage vector of the converter, including a first cost function for calculating the deviation between a reference current and the output current of the converter at the next moment, taking the number of switching times of the converter switching device as a second cost function, and taking the α-axis current of the output current of the converter at the next moment as a third cost function; calculating the first output value of the first cost function, and selecting a first number of preferred first output values ​​and a corresponding set of output voltage vectors therefrom; substituting the output voltage vector set into the second cost function and the third cost function, calculating the second output value and the third output value, taking the output voltage vector corresponding to the optimal second output value or the optimal third output value as the optimal output voltage vector, and controlling the converter based on the optimal output voltage vector.

[0006] Specifically, the current prediction function adopts the following formula:

[0007] i j (k+1)=(1-RT s / L)i j (k)+(T s / L)(V j (k)-e j (k)),

[0008] Among them, i j (k+1) and i j (k) represents the j-axis current of the converter output current at time k+1 and time k respectively, the j-axis represents the α-axis or the β-axis, R represents the equivalent resistance on a single phase, L represents the filter inductance on a single phase, T s Sampling period, V j (k) represents the j-axis voltage of the converter output voltage vector at time k, e j (k) represents the j-axis voltage of the grid voltage at time k.

[0009] Specifically, the first cost function G1 adopts the following formula:

[0010] G1=|i α *-i α (k+1)|+|i β *-i β (k+1)|,

[0011] Among them, i α * and i β*Represents the reference values ​​of α-axis current and β-axis current respectively.

[0012] Specifically, the first cost function G1 adopts the following formula:

[0013] G1=|i α *-i α (k+1)|+|i β *-i β (k+1)|+f(i α (k+1),i β (k+1)),

[0014] Among them, i α (k+1)>i max or β (k+1)>i max When f(i α (k+1),i β (k+1))=∞,i α (k+1)≤i max And i β (k+1)≤i max When f(i α (k+1),i β (k+1))=0, i max Indicates the converter output current threshold.

[0015] Specifically, the second cost function G2 adopts the following formula:

[0016] G2=n s ,

[0017] Among them, n s Indicates the number of switching times of the converter switching device.

[0018] Specifically, the third cost function G3 adopts the following formula:

[0019] G3=|i αf (k+1)|,

[0020] Among them, i αf (k+1) represents the α-axis filtered current of the converter output current at time k+1.

[0021] Specifically, a preferred screening step is performed on all the calculated second output values ​​until no second output value can be screened; the preferred screening step includes: if the second output value is less than the second threshold value, the corresponding second output value is screened as the preferred second output value, and the output voltage vector corresponding to the preferred second output value is placed in the array OT; if the number of elements in the array OT is less than the first standard number, the second threshold value is increased according to the standard ratio; if the number of elements in the array OT is greater than the second standard number, the second threshold value is reduced according to the standard ratio; if after the preferred screening step is executed, no preferred second output value is screened and the array OT is an empty set, the minimum second output value is used as the optimal second output value.

[0022] Specifically, a classification and screening step is performed on all the calculated third output values ​​until all the third output values ​​are classified; the classification and screening step includes: if the third output value is less than the third threshold value, the corresponding third output value is screened as a preferred third output value, and the output voltage vector corresponding to the preferred third output value is placed in an array BF; if the third output value is not less than the third threshold value, the corresponding third output value is screened as a second-selected third output value, and the output voltage vector corresponding to the second-selected third output value is placed in an array SF; if the number of elements in array BF is less than the first standard number, the third threshold is increased according to the standard ratio; if the number of elements in array BF is greater than the second standard number, the third threshold is reduced according to the standard ratio; in the intersection of array OT and array BF, the minimum second output value corresponding to the output voltage vector is used as the optimal second output value.

[0023] Specifically, if the intersection of the array OT and the array BF is an empty set, then in the intersection of the array OT and the array SF, the minimum third output value corresponding to the output voltage vector is used as the optimal third output value.

[0024] The present invention also provides a hybrid model predictive control device for a grid-type converter without weight factors, comprising: a virtual unit, a prediction unit, a cost function establishment unit, a first preferred unit and a second preferred unit, wherein: the virtual unit is used to control the converter to simulate the working mode of a synchronous generator and provide the output three-phase AC current to the power grid; the prediction unit is used to sample the output current, output voltage vector and power grid voltage of the converter, convert the current and voltage into αβ coordinate system and discretize them at the same time, and establish a current prediction function for predicting the output current of the converter at the next moment; the cost function establishment unit is used to establish the cost function of the converter output voltage vector, including calculating the reference current and the cost function of the next moment. The first preferred unit is used to calculate the first output value of the first cost function, and select a first number of preferred first output values ​​and a corresponding output voltage vector set therefrom; the second preferred unit is used to substitute the output voltage vector set into the second cost function and the third cost function, calculate the second output value and the third output value, and take the output voltage vector corresponding to the optimal second output value or the optimal third output value as the optimal output voltage vector, and control the converter based on the optimal output voltage vector.

[0025] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: the original cost function is divided into multiple cost functions, the cost functions are calculated separately, and the optimal parameters that can comprehensively meet the optimization of all cost functions are screened, effectively avoiding the negative impact of weights in the cost function. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A system diagram of a virtual synchronous machine provided by the present invention;

[0027] Figure 2 A control block diagram of a hybrid model predictive control is provided for the present invention;

[0028] Figure 3 A flow chart of the adaptive intersection criterion provided by the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0030] See also Figure 1 , which is a system diagram of the virtual synchronous machine provided by the present invention.

[0031] The present invention provides a hybrid model predictive control method for a grid-type converter without weight factors. In an embodiment of the present invention, the converter is controlled to simulate the working mode of a synchronous generator and output three-phase alternating current to a grid.

[0032] In a specific implementation, the control converter simulates the working mode of the synchronous generator, so that it has the same characteristics as the synchronous generator and realizes the functions of active frequency regulation and reactive voltage regulation. Figure 1 The input side of the converter (inverter) is set to U dc , converting DC current into three-phase AC current and outputting it to the grid through resistors and inductors.

[0033] In specific implementation, according to the swing equation of the core mathematical model of the synchronous generator, the mechanical characteristics of the VSG can be expressed as:

[0034] J(dω / dt)=T m -T e -Dω, formula (1),

[0035] Where J represents the moment of inertia, ω represents the actual angular velocity of the rotor, and T m Represents the mechanical torque of VSG, T e represents electromagnetic torque, and D represents damping coefficient.

[0036] The mechanical torque T of the synchronous generator can be calculated by formula (2) and (3): m and electromagnetic torque T e By adjusting T m and T e , the purpose of active frequency modulation can be achieved:

[0037] T m =P ref / ω n , formula (2),

[0038] T e =M f i f (i,sinθ), formula (3),

[0039] Among them, P ref Represents a given active power, ω n Represents the rated angular speed of the synchronous generator, M f represents the maximum mutual inductance between the field winding and the three-phase stator coil, i f Represents the rotor excitation current.

[0040] In the specific implementation, the actual reactive power Q of the power grid is calculated through the excitation current and mutual inductance to realize the function of reactive voltage regulation:

[0041] Q=-ωM f i f (i,cosθ), formula (4).

[0042] In a specific implementation, according to the virtual potential E and the terminal voltage V m The resulting voltage difference generates a reference current, and VSG thus behaves as a controlled current source:

[0043] I=(EV m ) / (R'+jωL'), formula (5),

[0044] Where I is the output current of the converter, R' is the total resistance of the grid, and L' is the total filter inductance of the grid.

[0045] In the embodiment of the present invention, the output current, output voltage vector and grid voltage of the converter are sampled.

[0046] In the specific implementation, according to the VSG equivalent diagram and Kirchhoff's voltage law, the voltage expression is obtained as follows:

[0047] L(di w / dt)+Ri w =V wn -e w , formula (6),

[0048] Where, w = a or b or c, indicating phase a or phase b or phase c, R indicates the equivalent resistance on a single phase, L indicates the filter inductance on a single phase, i w 、V wn and e w They respectively represent the w-phase converter output current, the w-phase converter output voltage and the w-phase grid voltage, which are obtained by sampling.

[0049] In the embodiment of the present invention, the current and voltage (the output current and output voltage of the converter) are converted into the αβ coordinate system and discretized at the same time, and a current prediction function for predicting the output current of the converter at the next moment is established.

[0050] In the embodiment of the present invention, the current prediction function adopts the following formula:

[0051] i j (k+1)=(1-RT s / L)i j (k)+(T s / L)(V j (k)-e j (k)), formula (7),

[0052] Among them, i j (k+1) and i j (k) represents the j-axis current of the converter output current at time k+1 and time k respectively, the j-axis represents the α-axis or the β-axis, R represents the equivalent resistance on a single phase, L represents the filter inductance on a single phase, T s Sampling period, V j(k) represents the j-axis voltage of the converter output voltage vector at time k, e j (k) represents the j-axis voltage of the grid voltage at time k. α i β , voltage e α e β ) is obtained through sampling.

[0053] In a specific implementation, the αβ coordinate system, also known as the stationary coordinate system, is a two-phase orthogonal coordinate system, which is usually used to convert a three-phase system into a two-phase system to simplify analysis. In this coordinate system, the α axis coincides with the a phase in the three-phase system, and the β axis leads the α axis by 90 electrical degrees.

[0054] In an embodiment of the present invention, a cost function of the converter output voltage vector is established, including a first cost function for calculating the deviation between a reference current and the converter output current at the next moment, taking the number of switching times of the converter switching device as a second cost function, and taking the α-axis current of the converter output current at the next moment as a third cost function.

[0055] See also Figure 2 , which is a control block diagram of a hybrid model predictive control provided by the present invention, wherein the first cost function is MPC1, the second cost function is MPC2, and the third cost function is MPC3.

[0056] In the embodiment of the present invention, the first cost function G1 adopts the following formula:

[0057] G1=|i α *-i α (k+1)|+|i β *-i β (k+1)|, formula (8),

[0058] Among them, i α * and i β *Represents the reference values ​​of α-axis current and β-axis current respectively.

[0059] In a specific implementation, the first cost function is a cost function for tracking current, which controls the output current to approach the reference current. α (k+1) and i β The calculation of (k+1) refers to formula (7), the α-axis current and β-axis current reference values ​​i α * and i β *, can be obtained through the active power reference value P set and reactive power reference value Q set get.

[0060] In the embodiment of the present invention, the first cost function G1 adopts the following formula:

[0061] G1=|i α *-i α (k+1)|+|i β *-i β (k+1)|+f(i α (k+1),i β (k+1)), formula (9),

[0062] Among them, i α (k+1)>i max or β (k+1)>i max When f(i α (k+1),i β (k+1))=∞,i α (k+1)≤i max And i β (k+1)≤i max When f(i α (k+1),i β (k+1))=0, i max Indicates the converter output current threshold.

[0063] In the specific implementation, f(i α (k+1),i β (k+1)) as a protection mechanism, and accordingly set the threshold i according to the actual application scenario. max , that is, the α-axis current or β-axis current at the next moment is greater than the threshold i max When , the first cost function takes a positive infinity value, the optimization fails, and the VSG stops working to prevent the device from being damaged by excessive current.

[0064] In the embodiment of the present invention, the second cost function G2 adopts the following formula:

[0065] G2=n s , formula (10),

[0066] Among them, n s Indicates the number of switching times of the converter switching device.

[0067] In a specific implementation, the switching times of the switch device is Figure 1 The number of converter switching devices g1 to g6 that are switched from closed to open, or from closed to closed at a certain time (eg, time k or time k+1).

[0068] In the embodiment of the present invention, the third cost function G3 adopts the following formula:

[0069] G3=|i αf (k+1)|, formula (11),

[0070] Among them, i αf (k+1) represents the α-axis filtered current of the converter output current at time k+1.

[0071] In a specific implementation, the optimization of the switching times of the converter switch device can reduce the loss, and the optimization of the filtered α-axis current can improve the current performance.

[0072] In the specific implementation, the multi-objective cost function G*=G1+G2+G3, formula (12), predicts optimization and controls G1, G2 and G3 respectively, which is the hybrid model predictive control.

[0073] See also Figure 3 , which is a flow chart of the adaptive intersection criterion provided by the present invention.

[0074] In a specific implementation, the adaptive intersection criterion is based on an output voltage vector set generated by a first cost function as MPC1, and substitutes relevant parameters of the output voltage vector set into a second cost function as MPC2 and a third cost function as MPC3 for calculation and screening to obtain an optimal output voltage vector, and controls the PWM generator based on relevant parameters of the optimal output voltage vector (closing or shutting down of the converter switching device), thereby controlling the converter switching device.

[0075] In the specific implementation, the calculation of the cost function is mainly based on the state of the switching devices of the converter. The closed and open states of the switching devices on the same bridge arm of the converter are different. The switching states of g1 and g2 are different, the switching states of g3 and g4 are different, and the switching states of g5 and g6 are different. Therefore, there are a total of 2×2×2=8 types. Correspondingly, 8 first output values ​​can be calculated by substituting them into the first cost function.

[0076] In the embodiment of the present invention, a first output value of a first cost function is calculated, and a first number of preferred first output values ​​and corresponding output voltage vector sets are selected therefrom.

[0077] In a specific implementation, as described above, the first cost function can calculate a total of 8 first output values, and the smaller the output value, the better, which means a better optimization effect. Therefore, the criterion for selecting is to select a smaller output value. A first number of preferred first output values ​​are selected, wherein the first number can be set according to the actual application scenario. In the embodiment of the present invention, the first number is set to 4, that is, the 8 first output values ​​are arranged from small to large, and the first 4 smaller first output values ​​are selected. These 4 first output values ​​correspond to 4 output voltage vectors, and these 4 output voltage vectors can be used as an output voltage vector set.

[0078] In an embodiment of the present invention, the output voltage vector set is substituted into the second cost function and the third cost function, the second output value and the third output value are calculated, the output voltage vector corresponding to the optimal second output value or the optimal third output value is used as the optimal output voltage vector, and the converter is controlled based on the optimal output voltage vector.

[0079] In a specific implementation, the optimal second output value or the optimal third output value, which is usually the minimum second output value and the minimum third output value, can be used as the optimal second output value and the optimal third output value.

[0080] In a specific implementation, in order to comprehensively consider the three cost functions, the present invention further limits the optimal second output value and the optimal third output value.

[0081] In an embodiment of the present invention, a preferred screening step is performed on all the calculated second output values ​​until no second output value can be screened (as a preferred second output value); the preferred screening step includes: if the second output value is less than the second threshold value, the corresponding second output value is screened as a preferred second output value, and the output voltage vector corresponding to the preferred second output value is placed in an array OT; if the number of elements in the array OT is less than the first standard number, the second threshold value is increased according to a standard ratio; if the number of elements in the array OT is greater than the second standard number, the second threshold value is reduced according to a standard ratio; if after the preferred screening step is executed, no preferred second output value is screened and the array OT is an empty set, the minimum second output value is used as the optimal second output value.

[0082] In a specific implementation, the output voltage vector set is substituted into the second cost function and the second cost function for calculation, that is, the output voltage vector related parameters (the closed or off state of the converter switching device) are substituted into the second cost function and the second cost function for calculation to obtain the corresponding second output value and third output value.

[0083] In a specific implementation, when executing the preferred screening step for the second output value, each time a second output value is compared with the second threshold, the second threshold is adjusted to be increased or decreased, and then the second output value is compared with the second threshold again. Each second output value can be compared with the second threshold multiple times, and the preferred screening step is stopped only when no second output value can be screened as the preferred second output value.

[0084] In a specific implementation, the second threshold g 2minIt is used to evaluate whether the optimization degree of the second cost function meets the standard. Its value can be set according to the actual application scenario. The first standard quantity and the second standard quantity can also be set according to the actual application scenario. In the embodiment of the present invention, they are set to 2 and 3 respectively. The standard ratio of the second threshold increase or decrease can be set according to the actual application scenario. In the embodiment of the present invention, it is set to 5%.

[0085] In a specific implementation, if after the optimization screening step, the array OT is an empty set, it indicates that the optimization result of the first cost function does not meet the optimization requirement of the second cost function (the second threshold g 2min ), the output voltage vector with the smallest second output value (optimal second output value) is directly selected from the optimization result of the first cost function, that is, from the four output voltage vectors in the previously mentioned output voltage vector set, as the optimal output voltage vector, and control is performed based on the relevant parameters of the optimal output voltage vector.

[0086] In a specific implementation, if after the optimization screening step, the array OT is not an empty set, it indicates that there are optimization results of the first cost function that can meet the optimization requirements of the second cost function (the second threshold g 2min )’s output voltage vector, therefore, the third cost function is further considered.

[0087] In an embodiment of the present invention, a classification and screening step is performed on all calculated third output values ​​until all third output values ​​are classified; the classification and screening step includes: if the third output value is less than the third threshold value, the corresponding third output value is screened as a preferred third output value, and the output voltage vector corresponding to the preferred third output value is placed in an array BF; if the third output value is not less than the third threshold value, the corresponding third output value is screened as a second-selected third output value, and the output voltage vector corresponding to the second-selected third output value is placed in an array SF; if the number of elements in array BF is less than the first standard number, the third threshold is increased according to the standard ratio; if the number of elements in array BF is greater than the second standard number, the third threshold is reduced according to the standard ratio; in the intersection of array OT and array BF, the minimum second output value corresponding to the output voltage vector is used as the optimal second output value.

[0088] In a specific implementation, when the classification and screening step is performed on the third output value, each time a third output value is compared with the third threshold, the third threshold is adjusted to be increased or decreased, and then the third output value is compared with the third threshold again. Each third output value can be compared with the third threshold multiple times. Only when all the third output values ​​are determined to be preferred third output values ​​or secondary third output values ​​and the corresponding output voltage vectors are all placed in the array BF or SF, the classification and screening step is stopped.

[0089] In a specific implementation, the third threshold g 3min It is used to evaluate whether the optimization degree of the third cost function meets the standard. Its value can be set according to the actual application scenario. The first standard quantity and the second standard quantity can also be set according to the actual application scenario. In the embodiment of the present invention, they are set to 2 and 3 respectively. The standard ratio of increasing or decreasing the third threshold can be set according to the actual application scenario. In the embodiment of the present invention, it is set to 5%.

[0090] In a specific implementation, when array OT is not an empty set, in order to comprehensively consider the first, second and third cost functions, the intersection between array OT and array BF is calculated, and the output voltage vector with the minimum corresponding second output value (optimal second output value) is selected from the intersection as the optimal output voltage vector, and control is performed based on the relevant parameters of the optimal output voltage vector.

[0091] In a specific implementation, if the intersection between the array OT and the array BF is an empty set, it indicates that there is no optimization result of the first cost function that can simultaneously meet the optimization requirements of the second cost function and the third cost function (the second threshold g 2min and the third threshold g 3min )’s output voltage vector, therefore, the first, second and third cost functions are considered with reduced criteria.

[0092] In the embodiment of the present invention, if the intersection of array OT and array BF is an empty set, then in the intersection of array OT and array SF, the minimum third output value corresponding to the output voltage vector is used as the optimal third output value, and the corresponding output voltage vector is used as the optimal output voltage vector.

[0093] The present invention also provides a hybrid model predictive control device for a grid-type converter without weight factors, comprising: a virtual unit, a prediction unit, a cost function establishment unit, a first preferred unit and a second preferred unit, wherein: the virtual unit is used to control the converter to simulate the working mode of a synchronous generator and provide the output three-phase AC current to the power grid; the prediction unit is used to sample the output current, output voltage vector and power grid voltage of the converter, convert the current and voltage into αβ coordinate system and discretize them at the same time, and establish a current prediction function for predicting the output current of the converter at the next moment; the cost function establishment unit is used to establish the cost function of the converter output voltage vector, including calculating the reference current and the cost function of the next moment. The first preferred unit is used to calculate the first output value of the first cost function, and select a first number of preferred first output values ​​and a corresponding output voltage vector set therefrom; the second preferred unit is used to substitute the output voltage vector set into the second cost function and the third cost function, calculate the second output value and the third output value, and take the output voltage vector corresponding to the optimal second output value or the optimal third output value as the optimal output voltage vector, and control the converter based on the optimal output voltage vector.

[0094] In a specific implementation, in the hybrid model predictive control device for a grid-type converter without weight factors provided by the present invention, an execution unit for executing methods, steps or functions, the methods, steps or functions executed by it can refer to the hybrid model predictive control method for a grid-type converter without weight factors provided by the present invention.

Claims

1. A hybrid model predictive control method for a grid-connected converter without weight factors, characterized in that: include: The control converter simulates the working mode of the synchronous generator and provides the output three-phase AC current to the power grid; The output current, output voltage vector and grid voltage of the converter are sampled, the current and voltage are converted into αβ coordinate system and discretized at the same time, and a current prediction function for predicting the output current of the converter at the next moment is established; Establishing a cost function of the converter output voltage vector, including a first cost function that calculates a deviation between a reference current and a converter output current at a next moment, taking the number of switching times of the converter switch device as a second cost function, and taking the α-axis current of the converter output current at the next moment as a third cost function; Calculating a first output value of a first cost function, and selecting a first number of preferred first output values ​​and a corresponding set of output voltage vectors therefrom; Substitute the output voltage vector set into the second cost function and the third cost function, calculate the second output value and the third output value, take the output voltage vector corresponding to the optimal second output value or the optimal third output value as the optimal output voltage vector, and control the converter based on the optimal output voltage vector.

2. The hybrid model predictive control method for grid-connected converter without weight factor according to claim 1, characterized in that: The current prediction function adopts the following formula: and j (k+1)=(1-RT s / L)i j (k)+(T s / L)(V j (k)-e j (k)), Among them, i j (k+1) and i j (k) represents the j-axis current of the converter output current at time k+1 and time k respectively, the j-axis represents the α-axis or the β-axis, R represents the equivalent resistance on a single phase, L represents the filter inductance on a single phase, T s Sampling period, V j (k) represents the j-axis voltage of the converter output voltage vector at time k, e j (k) represents the j-axis voltage of the grid voltage at time k.

3. The hybrid model predictive control method for grid-type converter without weight factor according to claim 2 is characterized in that: The first cost function G1 adopts the following formula: G1=|i α *-i α (k+1)|+|i β *-i β (k+1)|, Among them, i α * and i β *Represents the reference values ​​of α-axis current and β-axis current respectively.

4. The hybrid model predictive control method for grid-connected converter without weight factor according to claim 3 is characterized in that: The first cost function G1 adopts the following formula: G1=|i α *-i α (k+1)|+|i β *-i β (k+1)|+f(i α (k+1),i β (k+1)), Among them, i α (k+1)>i max or β (k+1)>i max When f(i α (k+1),i β (k+1))=∞,i α (k+1)≤i max And i β (k+1)≤i max When f(i α (k+1),i β (k+1))=0, i max Indicates the converter output current threshold.

5. The hybrid model predictive control method for grid-connected converter without weight factor according to claim 3, characterized in that: The second cost function G2 adopts the following formula: G2=n s , Among them, n s Indicates the number of switching times of the converter switching device.

6. The hybrid model predictive control method for grid-connected converter without weight factor according to claim 3, characterized in that: The third cost function G3 adopts the following formula: G3=|i αf (k+1)|, Among them, i αf (k+1) represents the α-axis filtered current of the converter output current at time k+1.

7. The hybrid model predictive control method for grid-connected converter without weight factor according to claim 1, characterized in that: The calculating the second output value and the third output value, and taking the output voltage vector corresponding to the optimal second output value or the optimal third output value as the optimal output voltage vector, includes: Performing a preferred screening step on all the calculated second output values ​​until no second output value can be screened; The preferred screening step includes: if the second output value is less than the second threshold, screening the corresponding second output value as the preferred second output value, and putting the output voltage vector corresponding to the preferred second output value into the array OT; if the number of elements in the array OT is less than the first standard number, increasing the second threshold according to the standard ratio; if the number of elements in the array OT is greater than the second standard number, reducing the second threshold according to the standard ratio; If no preferred second output value is obtained after the preferred screening step is completed, and the array OT is an empty set, the minimum second output value is taken as the optimal second output value.

8. The hybrid model predictive control method for grid-connected converter without weight factor according to claim 7, characterized in that: The calculating the second output value and the third output value, and taking the output voltage vector corresponding to the minimum second output value or the minimum third output value as the optimal output voltage vector, includes: Performing classification and screening steps on all calculated third output values ​​until all third output values ​​have been classified; The classification and screening step includes: if the third output value is less than the third threshold, the corresponding third output value is screened as the preferred third output value, and the output voltage vector corresponding to the preferred third output value is placed in the array BF; if the third output value is not less than the third threshold, the corresponding third output value is screened as the second selected third output value, and the output voltage vector corresponding to the second selected third output value is placed in the array SF; if the number of elements in the array BF is less than the first standard number, the third threshold is increased according to the standard ratio; if the number of elements in the array BF is greater than the second standard number, the third threshold is reduced according to the standard ratio; In the intersection of the array OT and the array BF, the minimum second output value corresponding to the output voltage vector is used as the optimal second output value.

9. The hybrid model predictive control method for grid-connected converter without weight factor according to claim 8, characterized in that: The calculating the second output value and the third output value, and taking the output voltage vector corresponding to the minimum second output value or the minimum third output value as the optimal output voltage vector, includes: If the intersection of the array OT and the array BF is an empty set, then in the intersection of the array OT and the array SF, the minimum third output value corresponding to the output voltage vector is used as the optimal third output value.

10. A hybrid model predictive control device for a grid-type converter without weight factors, characterized in that: include: A virtual unit, a prediction unit, a cost function establishment unit, a first preferred unit and a second preferred unit, wherein: The virtual unit is used to control the converter to simulate the working mode of the synchronous generator and provide the output three-phase AC current to the power grid; The prediction unit is used to sample the output current, output voltage vector and grid voltage of the converter, convert the current and voltage into αβ coordinate system and discretize them at the same time, and establish a current prediction function for predicting the output current of the converter at the next moment; The cost function establishing unit is used to establish a cost function of the converter output voltage vector, including a first cost function for calculating the deviation between the reference current and the converter output current at the next moment, taking the switching times of the converter switch device as the second cost function, and taking the α-axis current of the converter output current at the next moment as the third cost function; The first optimization unit is used to calculate a first output value of a first cost function, and select a first number of preferred first output values ​​and a corresponding set of output voltage vectors therefrom; The second optimization unit is used to substitute the output voltage vector set into the second cost function and the third cost function, calculate the second output value and the third output value, take the output voltage vector corresponding to the optimal second output value or the optimal third output value as the optimal output voltage vector, and control the converter based on the optimal output voltage vector.

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