Virtual synchronous machine control system based on rotational inertia self-adaption and model prediction

Abstract

The invention discloses a virtual synchronous machine control system based on rotational inertia self-adaption and model prediction, and relates to the technical field of power electronic and electrical equipment and electrical engineering, and the core of the virtual synchronous machine control system is that the virtual synchronous machine control system comprises a model prediction control module and a rotational inertia self-adaption control module; the model prediction control module builds a grid-connected inverter circuit according to the two-level inverter topological structure, builds each control link in combination with a VSG mathematical model to obtain a reference voltage, and obtains an optimal switch sequence control circuit through conversion, calculation and screening; compared with a traditional control method, the method has the advantages that the frequency stability of the micro-grid can be enhanced, the control performance can be optimized, complex calculation and parameter setting can be avoided, power decoupling can be realized, the problem of voltage interference of the power grid can be effectively solved, and the method is suitable for large-scale popularization and application. And powerful support is provided for stable and efficient operation of the micro-grid.

Description

Technical Field

[0001] The present invention relates to the technical fields of power electronic and electrical equipment and electrical engineering technology, and specifically to a virtual synchronous generator control system based on rotational inertia self - adaptation and model prediction. Background Art

[0002] In the field of micro - grid, inverters face the challenges of distributed energy diversity and micro - grid operation mode complexity, and multiple control strategies need to be adopted. Traditional control methods such as PQ constant - power control, VF constant - voltage - frequency control, and droop control are used. Inverters adopting these strategies lack rotational inertia and damping characteristics and cannot provide frequency and inertia support for the system. Therefore, the virtual synchronous generator (VSG) technology has emerged.

[0003] There are many problems in the underlying current - voltage double - closed loop of traditional VSG control algorithms: In terms of the control structure, it is difficult to eliminate grid - voltage interference in a three - phase system. Although introducing feed - forward control can alleviate it, it will increase the complexity; in the calculation process, the PI control of DC signals requires multiple coordinate transformations, and the PI parameter tuning is complex, time - consuming and laborious; in terms of performance impact, the change of control parameters has a significant impact on performance, and the parameters may need to be readjusted when the system operating conditions change; in addition, the rotational inertia and damping coefficient of traditional VSG control algorithms usually adopt fixed values. When the micro - grid suffers large power disturbances, the output frequency and active power of VSG fluctuate violently, and its rotational inertia cannot simultaneously balance active - power and frequency regulation; with the continuous increase of the penetration rate of distributed renewable energy, it is difficult for traditional VSG control technology to keep the system running quickly and stably. Therefore, improving VSG control technology has important research value. Summary of the Invention

[0004] The purpose of the present invention is to provide a virtual synchronous generator control system based on rotational inertia self - adaptation and model prediction to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A virtual synchronous generator control system based on rotational inertia self - adaptation and model prediction, where the virtual synchronous generator control system includes a model - predictive control module and a rotational - inertia self - adaptation control module;

[0006] The model - predictive control module is used to build a grid - connected inverter circuit and generate an optimal switching sequence for controlling the grid - connected inverter circuit according to the predicted voltage, and then control the virtual synchronous generator.

[0007] The rotational - inertia self - adaptation control module controls the virtual synchronous generator by adjusting the magnitudes of the rotational inertia and the damping coefficient.

[0008] Further, the model - predictive control module controls the virtual synchronous generator including:

[0009] Step S1: Build a grid-connected inverter circuit according to the two-level inverter topology structure;

[0010] Step S2: Build an active power frequency control link, a reactive power voltage regulation control link, and a virtual impedance link according to the VSG mathematical model;

[0011] Step S3: Obtain a reference voltage through the active power frequency control link, the reactive power voltage regulation control link, and the virtual impedance link, and the reference voltage is transformed by the Clark transformation to obtain two reference voltage vectors of the reference voltage in the orthogonal stationary coordinate system;

[0012] Step S4: According to the reference voltage vector, calculate the predicted voltage corresponding to each reference voltage in the next sampling period, and use the cost function to calculate the difference between the reference voltage and the predicted voltage at the next moment;

[0013] Step S5: Screen out the minimum difference between the reference voltage and the predicted voltage, obtain the corresponding voltage vector according to the minimum difference, and generate a switching sequence according to the voltage vector;

[0014] Step S6: Obtain a PWM waveform through the switching sequence to control the grid-connected inverter circuit.

[0015] Further, the two-level inverter topology structure is a three-phase two-level voltage source grid-connected inverter, and each phase of the inverter is controlled by a controller; when the control signal output by the controller is 1, the switch tube of the upper bridge arm of the corresponding inverter is turned on, and when the control signal output by the controller is 0, the switch tube of the lower bridge arm of the corresponding inverter is turned on; calculate the voltage value and voltage vector output by each phase of the inverter according to the control signal and the power supply voltage;

[0016] The above-mentioned three-phase two-level voltage source grid-connected inverter is the most commonly used grid-connected topology structure, which has a simple structure, a small filter volume, a low total harmonic distortion rate of the grid-connected current, and good dynamic performance;

[0017] The calculation method of the voltage value output by each phase of the inverter is:

[0018]

[0019] where, v an is the voltage value output by the a-phase inverter, v bn is the voltage value output by the b-phase inverter, v cn is the voltage value output by the c-phase inverter, S a is the control signal output by the a-phase inverter, S b is the control signal output by the b-phase inverter, S c is the control signal output by the c-phase inverter, Vdc is a DC power supply;

[0020] The calculation method of the voltage vector is as follows:

[0021]

[0022] where e is the voltage vector, represents the phase difference between phases;

[0023] When all three groups of control signals are 1 or all are 0, the upper bridge arm or the lower bridge arm conducts simultaneously. At this time, the inverter output is discontinuous and regarded as the zero state. The remaining control signal combinations are effective states. By changing the switching states of the inverter power switching tubes, the corresponding voltage vectors can be obtained; by adjusting the switching states of the power switching tubes, the output of the inverter can always be in the same amplitude, the same frequency and the same phase as the grid voltage, and finally grid connection is achieved.

[0024] Furthermore, the active frequency control link adjusts the frequency through the active frequency droop equation and the rotor motion equation;

[0025] The active frequency droop equation is:

[0026] P m = P ref + m(ω0 - ω);

[0027] where P ref is the mechanical power reference value, P m is the mechanical power, ω is the actual angular velocity, ω0 is the rated angular velocity, and m is the active power control coefficient;

[0028] The above active frequency droop equation mainly simulates the primary frequency regulation process of droop control;

[0029] Assuming that the virtual motor pole pair number is 1, at this time the mechanical angular velocity is equal to the electrical angular velocity, and the rotor motion equation can be expressed as:

[0030]

[0031] where J is the moment of inertia, D is the damping coefficient, P m is the mechanical power, P e is the electromagnetic power, ω is the actual angular velocity, ω0 is the rated angular velocity, and θ is the virtual power angle;

[0032] According to the active frequency droop equation and the rotor motion equation, the following formula can be obtained through Laplace transform:

[0033]

[0034] where S represents the complex frequency domain variable.

[0035] Furthermore, the reactive power and voltage control link adjusts the voltage through the reactive power and voltage droop equation;

[0036] The reactive power and voltage droop equation is as follows:

[0037]

[0038] where, E m is the internal electromotive force of the virtual synchronous machine, D q is the reactive power and voltage droop coefficient, K is the voltage gain, Q ref is the reactive power reference value, Q e is the actual reactive power, u n is the output voltage reference value, and u0 is the actual output voltage.

[0039] Considering the mathematical model of the actual exciter, the above equation includes a first-order inertia control link, while the traditional active power and voltage control equation only contains a proportional control link, which is prone to fluctuations or oscillations due to factors such as load disturbances, resulting in poor control performance.

[0040] Furthermore, the virtual impedance link obtains the power angle and amplitude through the active power and frequency link and the reactive power and voltage link respectively, and obtains the three-phase voltage value according to the power angle and the amplitude;

[0041] The three-phase voltage value is as follows:

[0042]

[0043] where, V* is the three-phase voltage value, θ is the power angle, and E is the amplitude.

[0044] Furthermore, in order to approximately obtain the characteristics of the synchronous machine, make the output characteristics of the inverter power source closer to those of the traditional synchronous machine, and at the same time facilitate power decoupling and achieve accurate power distribution, suppress circulating current, and improve the parallel operation stability of the virtual synchronous machine, a virtual impedance link is introduced, and the implementation method is as follows:

[0045]

[0046] where, v d_ref and v q_ref represent the components of the output voltage of the virtual synchronous machine in the dq coordinate axes, v d * is the component of the three-phase voltage value in the dq coordinate axes, R is the internal resistance of the inductor, i d is the component of the inverter output current on the d axis, i q is the component of the inverter output current on the q axis, ω is the angular frequency, and L is the inductor parameter.

[0047] Furthermore, the moment of inertia adaptive module adds the moment of inertia and damping coefficient to the virtual synchronous machine control link to achieve the regulation of the output frequency of the virtual synchronous machine;

[0048] The calculation method of the moment of inertia is as follows:

[0049] J = J0 + u1 * |ω - ω0| / (ω - ω0) * x;

[0050] where J is the moment of inertia, J0 is the original moment of inertia, u1 is the tracking coefficient, ω is the angular velocity, ω0 is the reference angular velocity, and x = dω / dt represents the angular acceleration;

[0051] The calculation method of the damping coefficient is as follows:

[0052] D = D0 + u2 * (ω - ω0);

[0053] where D is the damping coefficient, D0 is the original damping coefficient, and u2 is the tracking coefficient;

[0054] u1 and u2 in the above formula are adjusted according to the actual simulation effect.

[0055] Furthermore, the virtual synchronous machine control adopts an LC filter circuit. According to Kirchhoff's law, the mathematical model of the LC filter in the orthogonal stationary coordinate system is expressed as follows:

[0056]

[0057] where L is the filter inductor, C is the filter capacitor, U αβ represents the inverter-side voltage in the αβ coordinate system, i fαβ represents the inverter-side current in the αβ coordinate system, v αβ represents the load-side voltage in the αβ coordinate system, i αβ represents the load-side current in the αβ coordinate system, i Cαβ represents the current flowing through the filter capacitor in the αβ coordinate system;

[0058] When the signal sampling period is Ts, the first-order Euler formula is used to discretize the equation, and the discretized equation is:

[0059]

[0060] where k represents the kth sampling signal;

[0061] The present invention replaces the traditional current-voltage double closed-loop control with model predictive control and adds a moment of inertia adaptive control strategy to the virtual synchronous machine control.

[0062] Compared with the prior art, the beneficial effects of the present invention are:

[0063] 1. Enhance frequency stability

[0064] The moment of inertia adaptive module can dynamically adjust the moment of inertia and damping coefficient according to the system operating state. When the microgrid suffers from power disturbances, it can effectively suppress the violent fluctuations of the output frequency of the virtual synchronous machine, keep the system frequency relatively stable, improve the ability of the microgrid to cope with disturbances, and enhance the reliability of the system.

[0065] 2. Optimize control performance

[0066] The model predictive control module adopts an advanced control strategy. By accurately building the grid-connected inverter circuit and each control link, it uses the cost function to screen the optimal switching sequence. Compared with the traditional PI control, it avoids the complex coordinate transformation and the cumbersome PI parameter tuning process, reduces the calculation amount, improves the control accuracy and response speed, can better meet the control requirements under the complex working conditions of the microgrid, and improves the overall performance of the system.

[0067] 3. Achieve power decoupling

[0068] The virtual impedance link successfully realizes power decoupling, effectively solves the problem that it is difficult to eliminate the grid voltage interference in the three-phase system; by accurately obtaining the power angle and amplitude to calculate the three-phase voltage value and reasonably configuring each component, it reduces the mutual influence between various parts of the system, improves the stability and control accuracy of the system, and provides strong support for the stable and efficient operation of the microgrid. Brief description of the drawings

[0069] Figure 1 It is a schematic diagram of the model predictive control structure of the virtual synchronous machine control system based on moment of inertia adaptation and model prediction of the present invention;

[0070] Figure 2 It is a schematic diagram of the model predictive control flow of the virtual synchronous machine control system based on moment of inertia adaptation and model prediction of the present invention;

[0071] Figure 3 It is a schematic diagram of the DC power supply three-phase inverter grid-connected topology structure of the virtual synchronous machine control system based on moment of inertia adaptation and model prediction of the present invention;

[0072] Figure 4 It is a voltage vector diagram of the virtual synchronous machine control system based on moment of inertia adaptation and model prediction of the present invention;

[0073] Figure 5 It is a block diagram of the active frequency control of the virtual synchronous machine control system based on moment of inertia adaptation and model prediction of the present invention;

[0074] Figure 6This is the reactive power voltage regulation control block diagram of the virtual synchronous machine control system based on inertia adaptation and model prediction of the present invention;

[0075] Figure 7 This is the virtual impedance block diagram of the virtual synchronous machine control system based on inertia adaptation and model prediction of the present invention;

[0076] Figure 8 This is the comparison chart of the output voltage of the double closed-loop and model predictive control of the virtual synchronous machine control system based on inertia adaptation and model prediction of the present invention;

[0077] Figure 9 This is the comparison chart of the output frequency of the double closed-loop and model predictive control of the virtual synchronous machine control system based on inertia adaptation and model prediction of the present invention;

[0078] Figure 10 This is the comparison chart of the output active power of the double closed-loop and model predictive control of the virtual synchronous machine control system based on inertia adaptation and model prediction of the present invention;

[0079] Figure 11 This is the comparison chart of the output reactive power of the double closed-loop and model predictive control of the virtual synchronous machine control system based on inertia adaptation and model prediction of the present invention;

[0080] Figure 12 This is the inertia adaptation control block diagram of the virtual synchronous machine control system based on inertia adaptation and model prediction of the present invention;

[0081] Figure 13 This is the diagram of increasing the 30 kW load from 1 - 1.3 s of the virtual synchronous machine control system based on inertia adaptation and model prediction of the present invention;

[0082] Figure 14 This is the diagram of reducing the 15 kW load from 1.8 - 2.1 s of the virtual synchronous machine control system based on inertia adaptation and model prediction of the present invention. Specific embodiments

[0083] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0084] Embodiment: As Figures 1-14As shown in the figure, the present invention provides a technical solution, a virtual synchronous generator control system based on inertia self - adaptation and model prediction. The virtual synchronous generator control system includes a model predictive control module and an inertia self - adaptation control module;

[0085] The model predictive control module is used to build a grid - connected inverter circuit and generate an optimal switching sequence for controlling the grid - connected inverter circuit according to the predicted voltage, thereby controlling the virtual synchronous generator;

[0086] The inertia self - adaptation control module controls the virtual synchronous generator by adjusting the magnitudes of the inertia and damping coefficients;

[0087] Among them, the model predictive control module controls the virtual synchronous generator including:

[0088] Step S1: Build a grid - connected inverter circuit according to the two - level inverter topology;

[0089] Step S2: Build an active power - frequency control link, a reactive power voltage - regulation control link, and a virtual impedance link according to the VSG mathematical model;

[0090] Step S3: Obtain a reference voltage through the active power - frequency control link, the reactive power voltage - regulation control link, and the virtual impedance link. The reference voltage is transformed by Clark transformation to obtain two reference voltage vectors of the reference voltage in the orthogonal stationary coordinate system;

[0091] Step S4: According to the reference voltage vectors, calculate the predicted voltage corresponding to each reference voltage in the next sampling period, and use a cost function to calculate the difference between the reference voltage and the predicted voltage at the next moment;

[0092] Step S5: Screen out the minimum difference between the reference voltage and the predicted voltage, obtain the corresponding voltage vector according to the minimum difference, and generate a switching sequence according to the voltage vector;

[0093] Step S6: Obtain a PWM waveform through the switching sequence to control the grid - connected inverter circuit;

[0094] Among them, the two - level inverter topology is a three - phase two - level voltage - type grid - connected inverter, and each phase inverter is controlled by a controller; when the control signal output by the controller is 1, the switch tube of the upper bridge arm of the corresponding inverter is turned on, and when the control signal output by the controller is 0, the switch tube of the lower bridge arm of the corresponding inverter is turned on; calculate the voltage value and voltage vector output by each phase inverter according to the control signal and the power supply voltage;

[0095] The calculation method of the voltage value output by each phase inverter is:

[0096]

[0097] Among them, v an is the voltage value output by the inverter of phase a, v bn is the voltage value output by the inverter of phase b, v cn is the voltage value output by the inverter of phase c, S a is the control signal output by the inverter of phase a, S b is the control signal output by the inverter of phase b, S c is the control signal output by the inverter of phase c, V dc is the DC power supply;

[0098] The calculation method of the voltage vector is as follows:

[0099]

[0100] Among them, e is the voltage vector, represents the phase difference between phases;

[0101] In the embodiment of the present invention, the three-phase two-level inverter has a total of 8 different switching state combinations: 000 - 111, as Figure 4 shown, each switching state combination corresponds to an inverter output voltage vector, namely V0(0, 0, 0), V1(1, 0, 0), V2(1, 1, 0), V3(0, 1, 0), V4(0, 1, 1), V5(0, 0, 1), V6(1, 0, 1), V7(1, 1, 1); the magnitudes of all voltage vectors in the figure are equal, 6 voltage vectors are distributed in a regular hexagon, spaced 60° apart one by one, and V0(0, 0, 0) and V7(1, 1, 1) in the figure are zero voltage vectors placed at the origin;

[0102] Among them, the active frequency control link adjusts the frequency through the active frequency droop equation and the rotor motion equation;

[0103] The active frequency droop equation is:

[0104] P m = P ref + m(ω0 - ω);

[0105] Among them, P ref is the mechanical power reference value, P m is the mechanical power, ω is the actual angular velocity, ω0 is the rated angular velocity, and m is the active power control coefficient;

[0106] The rotor motion equation is:

[0107]

[0108] Among them, J is the moment of inertia, D is the damping coefficient, P m is the mechanical power, P e is the electromagnetic power, ω is the actual angular velocity, ω0 is the rated angular velocity, and θ is the virtual power angle;

[0109] According to the active power - frequency droop equation and the rotor motion equation, the following formula can be obtained through Laplace transform:

[0110]

[0111] Among them, S represents the complex - frequency - domain variable;

[0112] From the above formula, the control block diagram of the active - power link of the virtual synchronous machine can be obtained, as Figure 5 shown;

[0113] Among them, the reactive - voltage control link adjusts the voltage through the reactive - voltage droop equation;

[0114] The reactive - voltage droop equation is:

[0115]

[0116] Among them, E m is the internal electromotive force of the virtual synchronous machine, D q is the reactive - voltage droop coefficient, K is the voltage gain, Q ref is the reference reactive power, Q e is the actual reactive power, u n is the reference output voltage, and u0 is the actual output voltage;

[0117] From the above formula, the reactive - voltage control block diagram of the virtual synchronous machine can be obtained, as Figure 6 shown;

[0118] Among them, the virtual - impedance link obtains the power angle and amplitude through the active - power link and the reactive - voltage link respectively, and obtains the three - phase voltage value according to the power angle and the amplitude;

[0119] The three - phase voltage value is:

[0120]

[0121] Among them, V* is the three - phase voltage value, θ is the power angle, and E is the amplitude;

[0122] Among them, the virtual - impedance link is used to achieve power decoupling, and the implementation method of the power decoupling is as follows:

[0123]

[0124] Among them, v d_ref and v q_refRepresents the components of the virtual synchronous machine output voltage in the dq coordinate axes, v d * Are the components of the three-phase voltage values in the dq coordinate axes, R is the internal resistance of the inductor, i d Is the component of the inverter output current on the d axis, i q Is the component of the inverter output current on the q axis, ω is the angular velocity, L is the inductor parameter;

[0125] According to the above formula, the block diagram of the virtual impedance link can be obtained, as shown in Figure 7 Shown;

[0126] Among them, the moment of inertia adaptive module adds the moment of inertia and damping coefficient to the virtual synchronous machine control link to realize the control of the virtual synchronous machine;

[0127] The calculation method of the moment of inertia is as follows:

[0128] J = J0 + u1 * |ω - ω0| / (ω - ω0) * x;

[0129] Among them, J is the moment of inertia, J0 is the original moment of inertia, u1 is the tracking coefficient, ω is the angular velocity, ω0 is the reference angular velocity, and x = dω / dt represents the angular acceleration;

[0130] The calculation method of the damping coefficient is as follows:

[0131] D = D0 + u2 * (ω - ω0);

[0132] Among them, D is the damping coefficient, D0 is the original damping coefficient, and u2 is the tracking coefficient;

[0133] Among them, the virtual synchronous machine control uses an LC filter circuit. According to Kirchhoff's law, the mathematical model of the LC filter in the orthogonal stationary coordinate system is expressed as follows:

[0134]

[0135] Among them, L is the filter inductor, C is the filter capacitor, U αβ Represents the voltage measured by the inverter in the αβ coordinate system, i fαβ Represents the current on the inverter side in the αβ coordinate system, v αβ Represents the voltage on the load side in the αβ coordinate system, i αβ Represents the current on the load side in the αβ coordinate system, i Cαβ Represents the capacitance of the filter capacitor in the αβ coordinate system;

[0136] When the signal sampling period is Ts, the first-order Euler formula is used to discretize the equation, and the discretized equation is:

[0137]

[0138] Among them, k represents the k-th sampling signal.

[0139] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A virtual synchronous machine control system based on moment of inertia self - adaptation and model prediction, characterized in that: The virtual synchronous generator control system includes a model predictive control module and a rotational inertia adaptive control module; The model predictive control module is used to build a grid-connected inverter circuit and generate an optimal switching sequence for controlling the grid-connected inverter circuit according to the predicted voltage, thereby controlling the virtual synchronous generator; The rotational inertia adaptive control module controls the virtual synchronous generator by adjusting the magnitudes of the rotational inertia and damping coefficient.

2. The virtual synchronous machine control system based on moment of inertia self - adaptation and model prediction according to claim 1, characterized in that: The model predictive control module controls the virtual synchronous generator, including: Step S1: Build a grid-connected inverter circuit according to a two-level inverter topology; Step S2: Build an active power-frequency control link, a reactive power-voltage regulation control link, and a virtual impedance link according to the VSG mathematical model; Step S3: Obtain a reference voltage through the active power-frequency control link, the reactive power-voltage regulation control link, and the virtual impedance link. The reference voltage is transformed by the Clark transformation to obtain two reference voltage vectors of the reference voltage in the orthogonal stationary coordinate system; Step S4: According to the reference voltage vectors, calculate the predicted voltage corresponding to each reference voltage in the next sampling period, and use a cost function to calculate the difference between the reference voltage and the predicted voltage at the next moment; Step S5: Screen out the minimum difference between the reference voltage and the predicted voltage, obtain the corresponding voltage vector according to the minimum difference, and generate a switching sequence according to the voltage vector; Step S6: Obtain a PWM waveform through the switching sequence to control the grid-connected inverter circuit.

3. The virtual synchronous machine control system based on moment of inertia self - adaptation and model prediction according to claim 2, characterized in that: The two-level inverter topology is a three-phase two-level voltage-source grid-connected inverter, and each phase of the inverter is controlled by a controller; when the control signal output by the controller is 1, the switch tube of the upper bridge arm of the corresponding inverter conducts, and when the control signal output by the controller is 0, the switch tube of the lower bridge arm of the corresponding inverter conducts; calculate the voltage value and voltage vector output by each phase of the inverter according to the control signal and the power supply voltage; The calculation method of the voltage value output by each phase of the inverter is: Among them, v an is the voltage value output by the a-phase inverter, v bn is the voltage value output by the b-phase inverter, v cn is the voltage value output by the c-phase inverter, S a is the control signal output by the a-phase inverter, S b is the control signal output by the b-phase inverter, S c is the control signal output by the c-phase inverter, V dc is the DC power supply; The calculation method of the voltage vector is: where e is the voltage vector, representing the phase difference between phases.

4. The virtual synchronous machine control system based on moment of inertia adaptation and model prediction according to claim 2, characterized in that: The active power-frequency control link adjusts the frequency through the active power-frequency droop equation and the rotor motion equation; The active power-frequency droop equation is: P m = P ref + m(ω0 - ω); Among them, P ref is the mechanical power reference value, P m is the mechanical power, ω is the actual angular velocity, ω0 is the rated angular velocity, and m is the active power control coefficient; The rotor motion equation is: where J is the moment of inertia, D is the damping coefficient, P m is the mechanical power, P e is the electromagnetic power, ω is the actual angular velocity, ω0 is the rated angular velocity, and θ is the virtual power angle; According to the active power-frequency droop equation and the rotor motion equation, the following formula can be obtained through Laplace transform: where S represents a complex frequency domain variable.

5. The virtual synchronous machine control system based on moment of inertia self - adaptation and model prediction according to claim 2, characterized in that: The reactive power-voltage control link adjusts the voltage through the reactive power-voltage droop equation; The reactive power-voltage droop equation is: Among them, E m is the internal electromotive force of the virtual synchronous machine, D q is the reactive power droop coefficient, K is the voltage gain, Q ref is the reference reactive power, Q e is the actual reactive power, u n is the reference output voltage, and u0 is the actual output voltage.

6. The virtual synchronous machine control system based on moment of inertia self-adaptation and model prediction according to claim 2, wherein: The virtual impedance link respectively obtains the power angle and amplitude through the active power-frequency link and the reactive power-voltage link, and obtains the three-phase voltage values according to the power angle and the amplitude; The three-phase voltage values are: where V* is the three-phase voltage value, θ is the power angle, and E is the amplitude.

7. The virtual synchronous machine control system based on inertia self - adaptation and model prediction according to claim 2, characterized in that: The virtual impedance link is used to achieve power decoupling, and the implementation method of the power decoupling is as follows: Among them, v d_ref and v q_ref represent the components of the virtual synchronous machine output voltage in the dq coordinate axes, v d * is the component of the three-phase voltage value in the dq coordinate axes, R is the internal resistance of the inductor, i d is the component of the inverter output current on the d axis, i q is the component of the inverter output current on the q axis, ω is the angular velocity, and L is the inductor parameter.

8. The virtual synchronous machine control system based on moment of inertia self - adaptation and model prediction according to claim 1, characterized in that: The rotational inertia adaptive module adds the rotational inertia and damping coefficient to the virtual synchronous generator control link to achieve the control of the virtual synchronous generator; The calculation method of the rotational inertia is: J = J0 + u1 * |-ω0| / (ω - ω0) * x; Where, J is the moment of inertia, J0 is the original moment of inertia, u1 is the tracking coefficient, ω is the angular velocity, ω0 is the reference angular velocity, and x = dω / dt represents the angular acceleration; The calculation method of the damping coefficient is as follows: D = D0 + u2*(ω - ω0); Where, D is the damping coefficient, D0 is the original damping coefficient, and u2 is the tracking coefficient.

9. The virtual synchronous machine control system based on rotational inertia self - adaptation and model prediction according to claim 1, characterized in that: The virtual synchronous machine control adopts an LC filter circuit. According to Kirchhoff's law, the mathematical model of the LC filter in the orthogonal stationary coordinate system is expressed as follows: Among them, L is the filtering inductor, C is the filtering capacitor, U αβ represents the inverter-side voltage in the αβ coordinate system, i fαβ represents the inverter-side current in the αβ coordinate system, v αβ represents the load-side voltage in the αβ coordinate system, i αβ represents the load-side current in the αβ coordinate system, i Cαβ represents the capacitance of the filtering capacitor in the αβ coordinate system; When the signal sampling period is Ts, the first-order Euler formula is used to discretize the equation, and the discretized equation is: Where, k represents the k-th sampling signal.

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