NPC three-level VSG finite control set model predictive control method

Abstract

The application provides a NPC three-level VSG finite control set model predictive control method, comprising: establishing a rotor motion equation and a prime mover regulation equation, realizing active frequency control based on the rotor motion equation and the prime mover regulation equation to determine a power angle; establishing a reactive voltage droop characteristic equation, realizing reactive voltage control based on the reactive voltage droop characteristic equation to determine an amplitude; determining a three-phase voltage reference value according to the power angle and the amplitude, introducing a virtual impedance link, and determining a VSG output reference voltage according to the three-phase voltage reference value; establishing an LC filter model, and determining a capacitor voltage prediction value based on the LC filter model; constructing a cost function according to the VSG output reference voltage, the capacitor voltage prediction value and a direct current side midpoint voltage difference at a corresponding moment, and outputting an optimal voltage vector to control the VSG according to the cost function. The application solves the problem of poor frequency regulation performance of the traditional SVPWM control method.

Description

Technical Field

[0001] This invention relates to the field of virtual synchronous motor technology, and more specifically, to a predictive control method for an NPC three-level VSG finite control set model. Background Technology

[0002] Power electronic devices typically possess characteristics such as fast response speed and low inertia. Taking power electronic inverters as an example, because they lack the mechanical inertia and damping characteristics of traditional synchronous generators, they cannot provide inertia and damping for the system. Excessive integration will significantly reduce the system's inertia level, weaken its disturbance rejection capability, and in severe cases, even cause relay devices to malfunction. Virtual Synchronous Generator (VSG) technology combines the rotor motion equations and damping characteristics of synchronous generators with droop control, achieving frequency and voltage regulation characteristics similar to synchronous generators while providing inertia and damping for the system. This allows the inverter to have similar functions to traditional generators in terms of mechanism and external characteristics, which is beneficial for maintaining the operational stability of the power system.

[0003] For three-level VSG, traditional SVPWM (Space Vector Pulse Width Modulation) control requires complex dual-loop control, which is difficult to tune internal PI parameters and requires more sensors. When the islanded system is subjected to large power load switching, fixed-parameter VSG control can provide inertia and damping, but cannot guarantee frequency regulation performance. Summary of the Invention

[0004] The problem solved by this invention is that traditional SVPWM control requires at least one of the following: complex dual-loop control, difficulty in PI parameter tuning, need for more sensors, and poor frequency regulation performance.

[0005] To address the above problems, this invention provides an NPC three-level VSG finite control set model predictive control method, comprising:

[0006] Establish rotor motion equations and prime mover adjustment equations, and implement active frequency control based on the rotor motion equations and prime mover adjustment equations to determine the power angle. The virtual inertia and damping coefficient in the rotor motion equations are related to the angular velocity deviation and the rate of change of angular velocity. The virtual inertia and the damping coefficient are selected according to an adaptive control strategy, and the rate of change of angular velocity is extracted by the steepest differentiator.

[0007] Establish a reactive voltage droop characteristic equation, and implement reactive voltage control based on the reactive voltage droop characteristic equation to determine the amplitude.

[0008] The three-phase voltage reference value is determined based on the power angle and the amplitude. A virtual impedance circuit is introduced, and the VSG output reference voltage is determined based on the three-phase voltage reference value.

[0009] Establish an LC filter model, and determine the predicted value of the capacitor voltage based on the LC filter model;

[0010] A cost function is constructed based on the VSG output reference voltage, the predicted capacitor voltage, and the DC side midpoint voltage difference at the corresponding time. The optimal voltage vector is then selected and output based on the cost function to control the VSG.

[0011] Optionally, the rotor motion equation is expressed as:

[0012]

[0013] Where J represents the virtual inertia, D represents the damping coefficient, and P... m P represents mechanical power. e ω represents electromagnetic power, dω / dt represents the actual angular velocity, ω0 represents the rated angular velocity, ω-ω0 represents the angular velocity deviation, and θ represents the power angle.

[0014] Optionally, the prime mover adjustment equation is expressed as:

[0015] P m =P ref +m(ω0-ω);

[0016] Among them, P m P represents mechanical power. ref This represents the active power input reference value, and m represents the active power droop control coefficient.

[0017] The method of determining the power angle by implementing active frequency control based on the rotor motion equation and the prime mover adjustment equation includes: substituting the mechanical power determined by the prime mover adjustment equation into the rotor motion equation, and solving the equation simultaneously to determine the power angle.

[0018] Optionally, the reactive voltage droop characteristic equation is expressed as:

[0019] E=U N +n(Q ref -Q);

[0020] Where E represents the reactive power regulation output voltage, U N This represents the rated voltage, n represents the reactive voltage droop factor, and Q represents the rated voltage. ref Q represents the reactive power input reference value, and Q represents the reactive power output of VSG.

[0021] The method of determining the amplitude by implementing reactive voltage control based on the reactive voltage droop characteristic equation includes: taking the maximum absolute value of the reactive voltage regulation output voltage as the amplitude.

[0022] Optionally, the three-phase voltage reference value is expressed as:

[0023]

[0024] Among them, v * The three-phase voltage reference value is represented by E, the reactive voltage regulation output voltage is represented by θ, and the power angle is represented by θ.

[0025] Optionally, the virtual impedance element is represented as:

[0026]

[0027] Among them, v d_ref The d-axis component of the VSG output reference voltage, v d * and v q * represents the d-axis and q-axis components of the three-phase voltage reference value, respectively; R represents the resistance component of the virtual impedance; L represents the inductance component of the virtual impedance; and ω represents the fundamental angular velocity.

[0028] The step of determining the VSG output reference voltage based on the three-phase voltage reference value includes:

[0029] The VSG output reference voltage in the dq coordinate system is determined based on the three-phase voltage reference value.

[0030] The VSG output reference voltage is converted from the dq coordinate system to the αβ coordinate system.

[0031] Optionally, the LC filtering model is expressed as:

[0032]

[0033] Among them, U αβ Indicates the inverter-side voltage, i fαβ Indicates the inverter output current, i αβ Indicates load current measurement, v αβ Indicates the capacitor voltage, i Cαβ Indicates capacitor current;

[0034] The process of determining the predicted capacitor voltage based on the LC filtering model includes:

[0035] The LC filter model is discretized using the first-order Euler equation to obtain the inductor current prediction formula and the capacitor voltage prediction formula.

[0036] The predicted value of the capacitor voltage is obtained using the aforementioned capacitor voltage prediction formula.

[0037] Optionally, the NPC three-level VSG finite control set model predictive control method further includes: analyzing the DC-side capacitor midpoint voltage imbalance phenomenon and obtaining the DC-side midpoint voltage difference at the corresponding time by combining Kirchhoff's current law;

[0038] The DC side midpoint voltage difference is expressed as:

[0039]

[0040] Where Δu(k+1) and Δu(k) represent the voltage difference between the upper and lower capacitors on the DC side at time k+1 and time k, respectively, and T s The signal sampling period is represented by C1, the upper capacitor is represented by C2, C1 = C2, and i0(k) represents the midpoint current at time k.

[0041] Optionally, the cost function is expressed as:

[0042] g = |v α_ref -v α (k+1)|+|v β_ref -v β (k+1)|+λΔu(k+1);

[0043] Among them, v α_ref and v β_ref This indicates the VSG output reference voltage, v α (k+1) and v β (k+1) represents the capacitor voltage at time k+1, λ represents the DC side midpoint voltage weighting coefficient, and Δu(k+1) represents the voltage difference between the upper and lower capacitors on the DC side at time k+1.

[0044] The step of filtering and outputting the optimal voltage vector according to the cost function to control the VSG includes: outputting the voltage vector corresponding to the minimum cost function as the optimal voltage vector.

[0045] Optionally, the adaptive control strategy is expressed as:

[0046]

[0047] Where J represents the adaptive virtual inertia, J0 represents the virtual inertia in VSG steady state, D represents the adaptive damping coefficient, D0 represents the damping coefficient in VSG steady state, Δω represents the angular velocity deviation, k1 and k2 represent the virtual inertia adjustment coefficients, k3 and k4 represent the damping adjustment coefficients, and v2(t) represents the extraction of the rate of change of angular velocity using the steepest differentiator.

[0048] The NPC three-level VSG finite control set model predictive control method described in this invention uses MPC (Model Predictive Control) to replace the traditional dual-loop control. This effectively avoids the problem of poor control performance caused by the difficulty in tuning PI parameters in the voltage and current dual closed loop, and makes up for the complexity of PI parameter modulation. At the same time, compared with the traditional SVPWM control which requires more sensors, the use of MPC can reduce the use of DC-side current sensors, and has smaller DC-side voltage fluctuations and faster active power response speed. In addition, angular velocity deviation and rate of change are introduced into the parameter adaptive control. The fastest differential is used to extract the rate of change of angular velocity. By adaptively adjusting the virtual inertia and damping coefficient of the VSG system, the setting of piecewise functions and thresholds is eliminated, and frequent parameter fluctuations are avoided, which improves the frequency stability of the islanded system during power surges. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating the NPC three-level VSG finite control set model predictive control method according to an embodiment of the present invention.

[0050] Figure 2 This is a schematic diagram of the VSG topology according to an embodiment of the present invention;

[0051] Figure 3 Traditional VSG control block diagram;

[0052] Figure 4 This is a block diagram of active frequency control according to an embodiment of the present invention;

[0053] Figure 5 This is a block diagram of reactive voltage control according to an embodiment of the present invention;

[0054] Figure 6 This is a block diagram of a virtual impedance circuit according to an embodiment of the present invention;

[0055] Figure 7 This is a vector diagram of the inverter output voltage according to an embodiment of the present invention;

[0056] Figure 8 This is a diagram illustrating the midpoint potential imbalance analysis in an embodiment of the present invention.

[0057] Figure 9 This is a block diagram of the MPC-based VSG control according to an embodiment of the present invention;

[0058] Figure 10 This is a graph showing the power angle and angular velocity curves of a virtual synchronous generator according to an embodiment of the present invention.

[0059] Figure 11 A comparison of MPC control and SVPWM control in embodiments of the present invention. Figure 1 ;

[0060] Figure 12 A comparison of MPC control and SVPWM control in embodiments of the present invention. Figure 2 ;

[0061] Figure 13 A comparison of MPC control and SVPWM control in embodiments of the present invention. Figure 3 ;

[0062] Figure 14 A comparison of MPC control and SVPWM control in embodiments of the present invention. Figure 4 ;

[0063] Figure 15 A comparison of MPC control and SVPWM control in embodiments of the present invention. Figure 5 ;

[0064] Figure 16 The VSG control frequency waveform in an embodiment of the present invention Figure 1 ;

[0065] Figure 17 The VSG control frequency waveform in an embodiment of the present invention Figure 2 ;

[0066] Figure 18 The VSG control frequency waveform in an embodiment of the present invention Figure 3 ;

[0067] Figure 19 This is a comparison diagram of the effects of the fastest differentiator in this embodiment of the invention and a traditional differentiator. Detailed Implementation

[0068] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0069] like Figure 1 As shown, this embodiment of the invention provides an NPC three-level VSG finite control set model predictive control method, including:

[0070] Establish rotor motion equations and prime mover adjustment equations, and implement active frequency control based on the rotor motion equations and prime mover adjustment equations to determine the power angle. The virtual inertia and damping coefficient in the rotor motion equations are related to the angular velocity deviation and the rate of change of angular velocity. The virtual inertia and the damping coefficient are selected according to an adaptive control strategy, and the rate of change of angular velocity is extracted by the steepest differentiator.

[0071] Establish a reactive voltage droop characteristic equation, and implement reactive voltage control based on the reactive voltage droop characteristic equation to determine the amplitude.

[0072] The three-phase voltage reference value is determined based on the power angle and the amplitude. A virtual impedance circuit is introduced, and the VSG output reference voltage is determined based on the three-phase voltage reference value.

[0073] Establish an LC filter model, and determine the predicted value of the capacitor voltage based on the LC filter model;

[0074] A cost function is constructed based on the VSG output reference voltage, the predicted capacitor voltage, and the DC side midpoint voltage difference at the corresponding time. The optimal voltage vector is then selected and output based on the cost function to control the VSG.

[0075] Specifically, in combination Figure 2 As shown, the NPC (Neutral Point Clamped) three-level VSG system mainly includes a DC power supply, voltage divider capacitors, an NPC three-level converter, filter inductors, line impedance, filter capacitors, and a load. Among these, U... dc C1 and C2 are DC side voltages, L is filter inductor, R is line resistance, C is filter capacitor, ifabc is inductor current, vabc is capacitor voltage, and iabc is output current of VSG after LC filtering.

[0076] VSG frequency control is achieved through the rotor motion equation and the prime mover adjustment equation. Assuming the pole pair number is 1, the mechanical angular velocity equals the electrical angular velocity, and the rotor motion equation can be expressed as:

[0077]

[0078] Where J represents the virtual inertia, D represents the damping coefficient, and P... m P represents mechanical power. e ω represents electromagnetic power, dω / dt represents the actual angular velocity, ω0 represents the rated angular velocity, ω-ω0 represents the angular velocity deviation, and θ represents the virtual power angle.

[0079] Combination Figure 4 As shown, to more accurately simulate the characteristics of a synchronous generator, a prime mover regulation equation was added to the VSG frequency control. This can be obtained from the Pf droop characteristic:

[0080] P m =P ref +m(ω0-ω); (2)

[0081] Among them, P m P represents mechanical power. ref Let m represent the active power input reference value, and m represent the active power droop control coefficient. Solve equations (1) and (2) simultaneously to determine the power angle.

[0082] Combination Figure 5 As shown, the output voltage E of the reactive power voltage regulation can be obtained through the QU droop characteristic:

[0083] E=U N +n(Q ref -Q); (3)

[0084] Where E represents the reactive power regulation output voltage, U N This represents the rated voltage, n represents the reactive voltage droop factor, and Q represents the rated voltage. ref Q represents the reactive power input reference value, and Q represents the reactive power output of VSG. The maximum absolute value of the reactive power voltage regulation output voltage E is used as the amplitude.

[0085] After obtaining the power angle and amplitude from the active and reactive power loops of the VSG, the three-phase voltage reference value v* can be obtained:

[0086]

[0087] Among them, v * The three-phase voltage reference value is represented by E, the reactive voltage regulation output voltage is represented by θ, and the virtual power angle is represented by θ.

[0088] Combination Figure 6 As shown, to more closely simulate the quasi-static characteristics of a synchronous machine, a virtual impedance element is added before the three-phase voltage reference value is fed into the dual-loop control. Adding a virtual impedance element makes the output characteristics of the inverter power supply closer to those of a traditional synchronous machine, while also facilitating power decoupling and precise power distribution, suppressing circulating current, and improving the stability of the virtual synchronous machine in parallel operation. The virtual impedance element can be described as follows:

[0089]

[0090] Among them, v d_ref This represents the d-axis component of the VSG output reference voltage, v d * and v q * represents the d-axis and q-axis components of the three-phase voltage reference value, respectively; R represents the resistive component of the virtual impedance; L represents the inductive component of the virtual impedance; and ω represents the fundamental angular velocity. (Combined with...) Figure 9 As shown, after adding a virtual impedance element to determine the VSG output reference voltage in the dq coordinate system, it is transformed to the αβ coordinate system.

[0091] Combination Figure 9 As shown, since MPC (Model Predictive Control) has advantages such as good dynamic performance, simple control concept, and easy implementation of multi-objective optimization control, using MPC to replace the traditional dual-loop control can effectively avoid the problem of poor control effect caused by the difficulty in tuning PI parameters in the voltage and current dual closed loop.

[0092] The mathematical model for LC filtering based on model-predicted VSG can be expressed as:

[0093]

[0094] Among them, U αβ Indicates the inverter-side voltage, i fαβ Indicates the inverter output current, i αβ Indicates load current measurement, v αβ Indicates the capacitor voltage, i Cαβ This represents the capacitor current.

[0095] When the NPC-type three-level inverter is operating normally, each bridge arm has 3 different operating modes, and the switching states S of each bridge arm of the inverter are defined. i (i = a, b, c), can be represented by a function as:

[0096]

[0097] Combination Figure 7 As shown, considering that the NPC three-level inverter has 27 different switching states, 27 voltage vectors can be generated accordingly.

[0098] To predict the voltage of the filter capacitor, let the signal sampling period be T. s Discretizing equation (6) using the first-order Euler equation yields:

[0099]

[0100] Among them, i fαβ (k) represents the converter-side current in the kth sampling period, i αβ (k) represents the load current measured in the kth sampling period, v αβ (k) represents the filter capacitor voltage in the kth sampling period, U αβ (k) represents the voltage vector in the kth sampling period, i fαβ (k+1) represents the predicted inductor current value in the (k+1)th sampling period, v αβ (k+1) represents the predicted value of the capacitor voltage in the (k+1)th sampling period.

[0101] After rearranging equation (8), we get:

[0102]

[0103] Combining equations (8) and (9), the inductor current i fαβ (k) and capacitor voltage v αβ (k) can be used at time k to predict the inductor current i at time k+1. fαβ (k+1). By sampling the load current iαβ (k) can be used to further predict the capacitor voltage v at time k+1. αβ (k+1).

[0104] The NPC three-level inverter suffers from an imbalance in the midpoint potential of the DC-side capacitors. This imbalance manifests as uneven voltage division between the two capacitors with significant voltage deviation, leading to a deterioration in the output voltage waveform quality. Therefore, appropriate control of the DC-side capacitor voltages is necessary. The current direction flowing through the upper and lower voltage-dividing capacitors on the DC side and the direction of the midpoint current are as follows: Figure 8 As shown. Assume C1 = C2 (where C1 is the upper capacitor; C2 is the lower capacitor), u C1 u C2 Let i be the voltage across the two capacitors, i0 be the midpoint current, and i be the voltage across the two capacitors. C1 i C2 It can be represented as:

[0105]

[0106] Let the signal sampling period be T s Discretizing equation (10) using the first-order Euler equation yields:

[0107]

[0108] After rearranging equation (11), we get:

[0109]

[0110] According to Kirchhoff's current law, we can obtain:

[0111]

[0112] Also, C1 = C2. From equations (12) and (13), we can obtain:

[0113]

[0114] Where Δu(k+1) and Δu(k) represent the voltage difference between the upper and lower capacitors on the DC side at time k+1 and time k, respectively.

[0115] Based on the above formula, the cost function is set, and the optimal voltage vector control (VSG) is selected. The cost function g is expressed as:

[0116] g = |v α_ref -v α (k+1)|+|v β_ref -v β (k+1)|+λΔu(k+1);(15)

[0117] Among them, v α_refand v β_ref This indicates the VSG output reference voltage, v α (k+1) and v β (k+1) represents the capacitor voltage at time k+1, λ represents the DC side midpoint voltage weighting coefficient, which is conventionally taken as 0.8, and Δu(k+1) represents the voltage difference between the upper and lower capacitors on the DC side at time k+1.

[0118] The VSG output reference voltage value and the capacitor voltage prediction value are calculated by equations (5) and (9) respectively. The DC side midpoint voltage difference at time k+1 is predicted according to equation (14). The optimal voltage vector is selected and output by the cost function of equation (15) (for example, the voltage vector corresponding to the minimum cost function is output as the optimal voltage vector).

[0119] When an islanded system is subjected to a large power disturbance, the virtual synchronous generator control with fixed parameters can provide inertia and damping for the system, but its frequency regulation and control flexibility are poor. Therefore, this embodiment provides an improved adaptive virtual synchronous generator control strategy, which introduces frequency deviation and frequency change rate into the virtual parameters of the VSG, and adaptively adjusts the virtual inertia and damping coefficient of the VSG system to improve the frequency stability of the islanded system when power changes suddenly.

[0120] Combination Figure 10 The power angle characteristic curve of the system under disturbance, as well as the changes in the rotor angular velocity rate of change dω / dt and the rotor angular velocity deviation Δω, can be divided into four stages (i.e., ① to ④) within one oscillation cycle. In stage ①, the VSG rotor angular frequency ω is greater than the grid angular frequency ω0 and dω / dt>0. At this time, ω of the VSG continues to increase, and J needs to be increased to reduce Δω. At this time, Δω(dω / dt)>0. In stage ②, ω is still greater than ω0, but dω / dt<0. J should be decreased to make |dω / dt| larger, so that ω approaches ω0 more quickly. At this time, Δω(dω / dt)<0. Stages ③ and ④ are similar. At the same time, the frequency deviation of the system can be adjusted by controlling the damping coefficient D. As D increases, the frequency deviation of the system decreases. Therefore, when Δω changes, the damping coefficient D can be adjusted appropriately. The selection principles of J and D in different stages are shown in Table 1.

[0121] Table 1 - Selection principles of J and D at different stages

[0122]

[0123] Based on the above analysis, the values ​​of the adaptive moment of inertia J and the damping coefficient D should be related to Δω and dω / dt. According to the selection principles for the virtual moment of inertia and damping coefficient described in Table 1, the proposed adaptive control strategy is expressed as follows:

[0124]

[0125] Where J0 represents the virtual inertia of the VSG in steady state, D0 represents the damping coefficient of the VSG in steady state, Δω represents the angular velocity deviation, dω / dt represents the rate of change of angular velocity, k1 and k2 represent the virtual inertia adjustment coefficients, and k3 and k4 represent the damping adjustment coefficients.

[0126] Since differentiators are physically impossible to implement in industrial practice, approximation methods are generally used as substitutes, but these are easily overwhelmed by amplified noise components. When designing adaptive VSG parameters, if the measured signal ω is disturbed by noise, its derivative value is affected by the noise, leading to inaccurate adaptive parameters and ultimately preventing optimal control. The tracking differentiator is a single-input, multiple-output dynamic structure proposed to eliminate or reduce the noise amplification effect of classical differentiators. For an input signal v0(t), a second-order tracking differentiator will produce two output signals v1(t) and v2(t). v1(t) tracks the input signal v0(t), while v2(t) is actually the generalized derivative of v0(t) overcoming noise interference.

[0127] The second-order fastest discrete tracking differentiator is constructed as follows:

[0128]

[0129] In equation (17), fhan() is represented as:

[0130]

[0131] Where h represents the integration step size, and reducing h has a significant effect on suppressing noise amplification; r represents the speed factor, and the larger r is, the faster the tracking speed; h0 represents the filtering factor, and increasing the filtering factor can enhance the filtering effect.

[0132] Based on the above analysis, equation (16) can be expressed as:

[0133]

[0134] Where v2(t) represents the extraction of the rate of change of angular velocity using the steepest differentiator.

[0135] The extraction of dω / dt by the above differentiator can effectively reduce the problem of sudden change in the rate of change of angular velocity caused by small disturbances in angular velocity ω. It does not require piecewise function and threshold constraints. The adaptive control of parameters J and D can be completed by using only Equation (19). It has the advantages of simple control strategy and strong robustness.

[0136] This embodiment describes a parameter-adaptive NPC three-level VSG finite control set model predictive control strategy, which simplifies dual-loop control and overcomes the complexity of PI parameter modulation. It also reduces the use of DC-side current sensors, resulting in smaller DC-side voltage fluctuations and faster active power response. Furthermore, by introducing angular velocity deviation and rate of change into the parameter-adaptive control, and using the fastest differentiator to extract the rate of change of angular velocity, it eliminates the need for piecewise functions and threshold settings, avoids frequent parameter fluctuations, and improves the frequency stability of the islanded system during power surges.

[0137] Among them, combined Figure 3 As shown, traditional VSG closed-loop control mainly includes active frequency control and reactive voltage control, typically using load-side current i abc Voltage v abc The active power P output by the VSG was obtained after power calculation. e The voltage frequency and amplitude are obtained through the active and reactive power loops of the VSG, and the voltage reference value for the dual-loop control of the inverter is calculated through a virtual impedance circuit. The voltage reference value is then adjusted by the proportional-integral (PI) converter within the dual-loop control to obtain the two-phase voltage. Finally, the signal is fed back to the gate of the NPC inverter via space vector modulation (SVPWM) to complete the entire closed-loop system control. However, this method suffers from complex dual-loop control and difficulties in PI parameter modulation. Furthermore, when the islanded system is subjected to large power load switching, while fixed-parameter VSG control can provide inertia and damping, it cannot guarantee frequency regulation performance.

[0138] The following is a verification of the solution in this embodiment.

[0139] The mathematical model was simulated and verified using Matlab / Simulink simulation software. The results show that it can optimize frequency support in islanded systems and effectively improve system stability.

[0140] The system load is initially 20kW, increases by 10kW at 0.2s, and is cut off at 0.5s.

[0141] Combination Figures 11 to 15As shown, the system operates stably before 0.2s, with an output active power of 20kW and a relatively balanced DC-side midpoint voltage. Under SVPWM control, the voltage fluctuation is approximately 4V, while under MPC control, the voltage fluctuation is approximately 1.6V. Between 0.2 and 0.5s, a 10kW load is added to the system, increasing the load current. At this time, the DC-side voltage fluctuation under SVPWM control is approximately 6V, while under MPC control, the voltage fluctuation is approximately 2.6V. Under SVPWM control, the active power rises to a stable value in approximately 0.016s, while under MPC control, it takes 0.015s. After 0.6s, the added load is removed, and the DC-side voltage fluctuations of the two control methods stabilize to 4V and 1.6V, respectively. Under SVPWM control, the active power drops to a stable value in approximately 0.031s, while under MPC control, it takes 0.026s. The results indicate that MPC has a faster response speed in power regulation and smaller DC-side voltage fluctuations compared to SVPWM.

[0142] Combination Figure 16 As shown, using the J-adaptive control strategy, it can be seen that when the system power deviates from the rated value at t=0.2s, the adaptive parameter J increases, providing more inertia to the system, and the dynamic response of the system frequency deviating from the rated value becomes slower; when the system power recovers to the rated value at t=0.5s, the adaptive parameter J decreases, and the dynamic response becomes faster.

[0143] Combination Figure 17 As shown, by adopting the D adaptive control strategy, the adaptive damping coefficient D will change accordingly when the system power changes, providing appropriate damping for the system while reducing the frequency deviation from 0.23Hz to 0.2Hz, which is beneficial to improving the stability of the system.

[0144] Combination Figure 18 As shown, a J-D adaptive control strategy is adopted, which can simultaneously optimize dynamic response speed and frequency deviation, ensuring frequency regulation performance and flexible control, and improving the frequency stability of the islanded system during power surges.

[0145] Combination Figure 19 As shown, the VSG angular velocity change rate is calculated using a traditional differentiator and a steepest differentiator, and then applied to the adaptive control strategy of this embodiment, resulting in a comparison chart of the corresponding J and D values. In this embodiment, the angular velocity change rate calculated by the steepest differentiator will not experience abrupt changes in its derivative value due to minor angular velocity fluctuations during stable operation. Applying it to the calculation of adaptive parameters J and D avoids frequent parameter changes caused by sudden changes in the angular velocity change rate, thus achieving a certain filtering effect. When the load changes, the adaptive parameter J increases or decreases according to the system's frequency state. When the frequency deviates from the normal value, the adaptive parameter D increases to reduce the frequency deviation.

[0146] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for NPC three-level VSG finite control set model predictive control, characterized in that, include: Establish rotor motion equations and prime mover adjustment equations, and implement active frequency control based on the rotor motion equations and prime mover adjustment equations to determine the power angle. The virtual inertia and damping coefficient in the rotor motion equations are related to the angular velocity deviation and the rate of change of angular velocity. The virtual inertia and the damping coefficient are selected according to an adaptive control strategy, and the rate of change of angular velocity is extracted by the steepest differentiator. Establish a reactive voltage droop characteristic equation, and implement reactive voltage control based on the reactive voltage droop characteristic equation to determine the amplitude. The three-phase voltage reference value is determined based on the power angle and the amplitude. A virtual impedance circuit is introduced, and the VSG output reference voltage is determined based on the three-phase voltage reference value. Establish an LC filter model, and determine the predicted value of the capacitor voltage based on the LC filter model; A cost function is constructed based on the VSG output reference voltage, the predicted capacitor voltage, and the DC side midpoint voltage difference at the corresponding time. The optimal voltage vector is then selected and output based on the cost function to control the VSG. The adaptive control strategy is expressed as follows: ； Where J represents the adaptive virtual inertia, J0 represents the virtual inertia in VSG steady state, D represents the adaptive damping coefficient, D0 represents the damping coefficient in VSG steady state, Δω represents the angular velocity deviation, k1 and k2 represent the virtual inertia adjustment coefficients, k3 and k4 represent the damping adjustment coefficients, and v2(t) represents the signal obtained by extracting the rate of change of angular velocity through a second-order fastest discrete tracking differentiator.

2. The NPC three-level VSG finite control set model predictive control method according to claim 1, characterized in that, The rotor motion equation is expressed as: ； wherein J represents the virtual inertia, D represents the damping coefficient, P m represents the mechanical power, P e represents the electromagnetic power, ω represents the actual angular velocity, dω / dt represents the angular velocity change rate, ω 0 represents the rated angular velocity, ω-ω 0 represents the angular velocity deviation, θ represents the power angle.

3. The NPC three-level VSG finite control set model predictive control method according to claim 2, characterized in that, The prime mover adjustment equation is expressed as follows: ； wherein, P m represents the mechanical power, P ref represents the active input reference value, m represents the active droop control coefficient; The method of determining the power angle by implementing active frequency control based on the rotor motion equation and the prime mover adjustment equation includes: substituting the mechanical power determined by the prime mover adjustment equation into the rotor motion equation, and solving the equation simultaneously to determine the power angle.

4. The NPC three-level VSG finite control set model predictive control method of claim 1, wherein, The reactive voltage droop characteristic equation is expressed as follows: ； wherein, E represents a reactive voltage regulating output voltage, U N represents a rated voltage, n represents a reactive voltage droop coefficient, Q ref represents a reactive input reference value, Q represents a VSG output reactive; The method of determining the amplitude by implementing reactive voltage control based on the reactive voltage droop characteristic equation includes: taking the maximum absolute value of the reactive voltage regulation output voltage as the amplitude.

5. The NPC three-level VSG finite control set model predictive control method according to claim 4, characterized in that, The three-phase voltage reference value is expressed as follows: ； wherein, v * represents the three-phase voltage reference value, E represents the reactive voltage regulation output voltage, θ represents the power angle.

6. The NPC three-level VSG finite control set model predictive control method according to claim 5, characterized in that, The virtual impedance element is represented as follows: ； wherein v d_ref represents the d-axis component of the VSG output reference voltage, v d * and v q * represents the d-axis component and the q-axis component of the three-phase voltage reference, respectively, R represents the resistance component in the virtual impedance, L represents the inductance component in the virtual impedance, ω represents the fundamental angular velocity; The step of determining the VSG output reference voltage based on the three-phase voltage reference value includes: The VSG output reference voltage in the dq coordinate system is determined based on the three-phase voltage reference value. The VSG output reference voltage is converted from the dq coordinate system to the αβ coordinate system.

7. The NPC three-level VSG finite control set model predictive control method according to claim 1, characterized in that, The LC filtering model is expressed as follows: ； wherein, U αβ represents an inverter-side voltage, i fαβ represents an inverter output current, i αβ represents a load measurement current, v αβ represents a capacitor voltage, i Cαβ represents a capacitor current; The process of determining the predicted capacitor voltage based on the LC filtering model includes: The LC filter model is discretized using the first-order Euler equation to obtain the inductor current prediction formula and the capacitor voltage prediction formula. The predicted value of the capacitor voltage is obtained using the aforementioned capacitor voltage prediction formula.

8. The NPC three-level VSG finite control set model predictive control method of claim 1, wherein, Also includes: The voltage imbalance at the midpoint of the DC-side capacitor is analyzed, and the voltage difference at the corresponding moment is obtained by combining Kirchhoff's current law. The DC side midpoint voltage difference is expressed as: ； in, u ( k +1) and u ( k The numbers () represent the voltage differences between the upper and lower capacitors on the DC side at times k+1 and k, respectively. T s Indicates the signal sampling period. C 1 indicates the upper capacitor. C 2 indicates the lower capacitor. C 1= C 2, i 0(k) represents the midpoint current at time k.

9. The NPC three-level VSG finite control set model predictive control method of claim 1, wherein, The cost function is expressed as: ； in, v α_ref and v β_ref This indicates the VSG output reference voltage. v α ( k +1) and v β ( k +1) represents the capacitor voltage at time k+1. λ This represents the weighting coefficient of the DC side midpoint voltage. u ( k +1) represents the voltage difference between the upper and lower capacitors on the DC side at time k+1; The filtering and outputting of the optimal voltage vector according to the cost function to control the VSG comprises: outputting the voltage vector corresponding to the minimum of the cost function as the optimal voltage vector.

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