MMC parameter collaborative optimization control method and system based on network-type inverter

Abstract

The present application relates to the field of new energy power generation technology, provide a kind of MMC parameter collaborative optimization control method and system based on network type inverter, comprising: obtaining system model parameters and network type inverter control parameters, establish the state space model of network type inverter and discretization, obtain the discretization state space model of network type inverter, construct the multi-objective optimization function of the weighted sum of power tracking target, parameter increment target, virtual speed target and overshoot target for the discretization state space model of network type inverter, solve multi-objective optimization function, obtain the optimal control reference value of network type inverter control layer;Based on control reference value, network type inverter control layer generates target voltage reference, based on target voltage reference, MMC control layer carries out power injection to modular multilevel converter.Effectively improve the overall power system operation reliability and control performance.

Description

Technical Field

[0001] This invention belongs to the field of new energy power generation technology, and in particular relates to a collaborative optimization control method and system for MMC parameters based on grid-connected inverters. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] As the penetration rate of new energy power generation in the power system continues to increase, grid-type inverters (such as virtual synchronous generators, VSGs) have been widely studied because they can actively maintain the stability of grid voltage and frequency. In addition, modular multilevel converters (MMCs) are also widely used in flexible DC transmission, new energy grid connection and other scenarios due to their advantages such as high output voltage quality and scalable structure.

[0004] In the existing VSG control strategy, the active control loop's moment of inertia J, damping coefficient D, and reactive control loop's integral coefficient K are... q The fixed values ​​limit the stability and adaptability of the power system under complex operating conditions. In particular, when faced with large disturbances or rapid changes in power commands, fixed parameter configurations may cause system oscillations, response delays, or even control instability. In addition, traditional control methods rarely consider the coordination relationship between control parameters and cannot adaptively adjust according to the operating state of the power system, thus affecting the performance of grid-connected inverters. Summary of the Invention

[0005] To address the technical problems mentioned above, this invention provides a collaborative optimization control method and system for MMC parameters based on grid-connected inverters. By establishing a discretized state-space model of the grid-connected inverter, constructing a multi-objective optimization function, and solving for the optimal control reference value, the invention achieves collaborative optimization of multiple indicators such as power point tracking, virtual speed, and overshoot suppression. Then, a hierarchical collaborative control strategy is adopted, with the inverter control layer generating the target voltage reference and the MMC control layer completing precise power injection. This solves the problems of fixed parameters and limited response performance in existing VSG control, significantly improving the dynamic response speed and regulation accuracy of the power system, enhancing grid connection stability, anti-disturbance capability, and fault adaptability, and effectively improving the overall reliability and control performance of the power system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of the present invention provides a method for collaborative optimization control of MMC parameters based on a grid-connected inverter, comprising: Obtain system model parameters and grid-type inverter control parameters, establish and discretize the state space model of the grid-type inverter to obtain the discretized state space model of the grid-type inverter, construct a multi-objective optimization function for the discretized state space model of the grid-type inverter consisting of a power tracking objective, a parameter increment objective, a virtual speed objective, and an overshoot objective, solve the multi-objective optimization function to obtain the optimal control reference value of the grid-type inverter control layer; Based on the control reference value, the grid-type inverter control layer generates a target voltage reference, and based on the target voltage reference, the MMC control layer injects power into the modular multilevel converter.

[0008] Furthermore, the multi-objective optimization function includes: ; ; ; ; in, Step size, for Output active power at any time To output active power reference value, , and For weight parameters, , , and They are respectively Moment , , and , , , and They are respectively , , and The increment, Where J is the virtual rotor speed, and J is the moment of inertia of the active control loop. The damping coefficient is... The proportional-integral coefficient of the reactive power control loop. for The overshoot of the second-order system at time t.

[0009] Furthermore, before solving the multi-objective optimization function, for conflicting objective functions, a Pareto optimization problem is constructed using the gradient method for joint optimization.

[0010] Furthermore, when solving the multi-objective optimization function, the following constraints are set: ; ; ; ; ; in, These are the moment of inertia J of the active control loop and the damping coefficient, respectively. Proportional-integral coefficient of reactive power control loop The initial value; , and These are the minimum allowable values ​​for each control parameter; , and These are the minimum allowable values ​​for each control parameter; , and They are respectively Moment , and , , and They are respectively , and The increment, It is an intermediate variable.

[0011] Furthermore, the system model parameters include grid-side line resistance and reactance, output active power, reactive power, active power command, reactive power command, and grid connection point voltage; The control parameters of the grid-type inverter include the port voltage and phase angle of the MMC output, as well as the virtual rotor speed of the grid-type inverter, the moment of inertia of the active control loop, the damping coefficient, and the proportional-integral coefficient of the reactive control loop. The control reference values ​​of the grid-type inverter control layer include reference values ​​for changes in moment of inertia, changes in damping coefficient, and changes in the proportional-integral coefficient of the reactive power control loop.

[0012] Furthermore, the grid-type inverter control layer includes an active-frequency control loop and a reactive-frequency control loop.

[0013] Furthermore, the MMC control layer includes: obtaining a reference value for the output current by using the target voltage reference and the grid connection point voltage through an electromagnetic equation model; using the reference value for the output current as the input for the MMC inner loop current control to obtain a differential mode voltage; adding the differential mode voltage and the common mode voltage obtained by MMC interphase circulating current suppression to obtain a lower arm voltage reference value; subtracting the differential mode voltage and the common mode voltage obtained by MMC interphase circulating current suppression to obtain an upper arm voltage reference value; and generating pulses by using the lower arm voltage reference value and the upper arm voltage reference value through NLM control and submodule voltage equalization control to control the switching of IGBTs in the modular multilevel converter.

[0014] A second aspect of the present invention provides a collaborative optimization control system for MMC parameters based on a grid-connected inverter, comprising: The control reference optimization module is configured to: acquire system model parameters and grid-type inverter control parameters; establish and discretize the state space model of the grid-type inverter to obtain the discretized state space model of the grid-type inverter; construct a multi-objective optimization function for the discretized state space model of the grid-type inverter, consisting of a weighted sum of power tracking target, parameter increment target, virtual speed target, and overshoot target; solve the multi-objective optimization function to obtain the optimal control reference value of the grid-type inverter control layer; The control module is configured to: generate a target voltage reference for the grid-type inverter control layer based on the control reference value; and inject power into the modular multilevel converter based on the target voltage reference.

[0015] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the MMC parameter collaborative optimization control method for grid-connected inverters as described above.

[0016] A fourth aspect of the present invention provides a computer device including a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, wherein the processor executes the program to implement the steps in the MMC parameter collaborative optimization control method based on a grid-connected inverter as described above.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention establishes a discretized state-space model of a grid-connected inverter, constructs a multi-objective optimization function, and solves for the optimal control reference value. This achieves synergistic optimization of multiple indicators such as power point tracking, virtual speed, and overshoot suppression. Then, a hierarchical collaborative control strategy is adopted, in which the inverter control layer generates the target voltage reference, and the MMC control layer completes precise power injection. This solves the problems of fixed parameters and limited response performance in existing VSG control, significantly improves the dynamic response speed and regulation accuracy of the power system, enhances grid-connected stability, anti-disturbance capability, and fault adaptability, and effectively improves the overall reliability and control performance of the power system.

[0018] This invention constructs a multi-objective optimization function that includes virtual speed response speed and system overshoot, and combines Pareto optimality strategy to solve conflicting objectives. At the same time, through second-order system small-signal analysis, it constructs the coupling relationship and constraint boundary between parameters, ensuring that the controller always operates within the system's stable operating domain on the multi-objective optimization path. This greatly improves the fault tolerance and control accuracy of grid-connected inverter systems and is suitable for the promotion and implementation of grid-connected inverter control strategies in high-proportion new energy grid-connected scenarios.

[0019] This invention combines the MMC inner current loop with phase-to-phase circulating current suppression control to construct a complete MMC control structure. The voltage reference output of the grid-type inverter control layer is further calculated to obtain the line reference current, which is then input to the MMC inner loop control to obtain the differential mode voltage. The MMC control part maintains a high-speed dynamic response, ensuring the accurate execution of the grid-type inverter control results, thereby supporting the voltage stability of the power system. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1 This is a flowchart of obtaining the control reference value of the VSG control layer according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of the VSG control layer and MMC control layer in Embodiment 1 of the present invention; Figure 3 This is a comparative simulation diagram of the moment of inertia, damping coefficient, and proportional-integral coefficient of the reactive power control loop in Embodiment 1 of the present invention. Figure 4 This is a simulation diagram comparing the step active power of Embodiment 1 of the present invention; Figure 5 This is a simulation diagram comparing the step reactive power of Embodiment 1 of the present invention; Figure 6 This is a simulation diagram comparing the frequency fluctuations of Embodiment 1 of the present invention; Figure 7 This is a schematic diagram of the structure of a computer device according to Embodiment 4 of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0023] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] Example 1 This embodiment provides a collaborative optimization control method for MMC parameters based on grid-type inverters.

[0025] The MMC parameter collaborative optimization control method based on grid-connected inverters provided in this embodiment aims to achieve adaptive collaborative optimization control of MMC parameters, solve the problems of fixed parameters and limited response performance in existing VSG control, improve the dynamic response performance and grid connection stability of the power system, and enhance the dynamic performance and fault adaptability of the power system.

[0026] In the existing VSG control strategy, the moment of inertia J and damping coefficient of the active control loop are... Proportional-integral coefficient of reactive power control loop Most parameters are fixed values, which limits the stability and adaptability of the system under complex operating conditions. Especially when facing large disturbances or rapid changes in power commands, fixed parameter configurations may cause system oscillations, response delays, or even control instability. This invention includes establishing an MMC system model with a VSG control structure and determining the key parameters that need to be adjusted during the system's dynamic process: the moment of inertia J of the active power control loop and the damping coefficient in the active power control loop. Proportional-integral coefficient of reactive power control loop Based on the Model Predictive Controller (MPC), a multi-objective optimization problem involving dynamic overshoot and response speed is constructed. A Pareto optimal solution strategy is employed to coordinate the adjustment of the active control loop's moment of inertia J and damping coefficient. Proportional-integral coefficient of reactive power control loop To address the challenge of balancing response speed and overshoot in traditional control systems and optimize system dynamic performance, this study analyzes the moment of inertia J and damping coefficient of the active control loop, taking into account the characteristics of a second-order underdamped system. Proportional-integral coefficient of reactive power control loop The impact on system stability is addressed by establishing parameter constraint boundaries to ensure both speed and stability during the system's tracking of active and reactive power commands. This enables a parameter adaptive and coordinated adjustment mechanism for VSG stability control, which significantly improves the dynamic performance and fault adaptability of the virtual synchronous machine control system while ensuring the stable operation of the MMC system.

[0027] The MMC parameter collaborative optimization control method based on grid-type inverters provided in this embodiment includes: Step 1: Use a Model Predictive Controller (MPC) to perform online collaborative optimization and adjustment of the control parameters of the VSG control layer to obtain the control reference values ​​for the VSG control layer. For example... Figure 1 As shown, the specific steps include: S101, Acquire the initial values ​​of the system model parameters and VSG control parameters required by the Model Predictive Controller (MPC).

[0028] The system model parameters include grid-side line resistance and reactance, and output active power. reactive power Active power command Reactive power command Grid connection point voltage .

[0029] The VSG control parameters include the port voltage output by the MMC converter. Phase angle and VSG virtual rotor speed The moment of inertia J of the active control loop and the damping coefficient Proportional-integral coefficient of reactive power control loop .

[0030] Based on the initial values ​​of the system model parameters and VSG control parameters required by the model predictive controller (MPC) in step S101, and combined with the small-signal analysis model, the constraint relationship between the moment of inertia J and the damping coefficient D of the active control loop is determined: ; ; ; ; ; ; in, This refers to the damping coefficient. In practical engineering applications, a damping value between 0.4 and 0.8 yields the best results. As an intermediate variable, and Power command and The relevant steady-state equilibrium point, This is the rated speed of the virtual rotor. For the network-side line impedance, The impedance phase angle, For the virtual synchronous machine rotor speed, For grid-side line inductance, This refers to the line resistance on the grid side.

[0031] S102, Based on the system model parameters and VSG control parameters, establish the state-space model of VSG and discretize it to obtain the discretized state-space model of VSG.

[0032] The function expression of the VSG state-space model established in step S102 of this embodiment is: ; in, for First-order differential, state variable Input variables , , , , , and They are respectively , , , , , The increment, For output, , and Let F be the state matrix, input matrix, and output matrix, respectively, with F being a constant term, and we have: ; ; ; ; in, To output active power relative to output phase angle The sensitivity coefficient with respect to the MMC port electromotive force E. These are the moment of inertia J of the active control loop and the damping coefficient, respectively. Proportional-integral coefficient of reactive power control loop initial value, VSG virtual speed initial value, , To output active power With reactive power initial value, The equivalent time constant for control parameter transmission is given by: ; ; ; ; in, and These are the grid-side line resistance and reactance, respectively. This is the phase angle.

[0033] Discretize the state-space model of the continuous VSG described above to obtain the discretized state-space model of the VSG: ; ; in, Let be the first derivative of the state variable at time k+1. Let k be the state variable at time k. Let k be the input variable at time k. This is the output at time k. , and Let be the discretized state matrix, input matrix, and output matrix, respectively, and have... , , , The sampling period.

[0034] S103 constructs a multi-objective optimization function for the discretized state-space model of VSG, consisting of a weighted sum of power tracking objective, parameter increment objective, virtual speed objective, and overshoot objective, and designs specified constraints.

[0035] In this embodiment, the multi-objective optimization function in step S103 is composed of a first objective function corresponding to the power tracking objective, a second objective function corresponding to the parameter increment objective, a third objective function corresponding to the virtual speed objective, and a fourth objective function corresponding to the overshoot objective.

[0036] The function expression for the first objective function is: ; The function expression for the second objective function is: ; The expression for the third objective function is: ; The function expression for the fourth objective function is: ; Where Np is the step size, P g (k) represents the output active power at time k. To output active power reference value, , and For weight parameters, , , and Let them be at time k respectively , , and , The overshoot of the second-order system at time k; the specified constraints include... , , The maximum and minimum constraints of the three; The function expression for calculating the overshoot of a second-order system is as follows: ; ; ; in, Let k be the initial value of the overshoot of the second-order system. Let k be the initial value of the damping ratio of the second-order system at time k. Let be the change in damping coefficient at time k.

[0037] In step S103 of this embodiment, when constructing a multi-objective optimization function consisting of a weighted sum of the power tracking objective, parameter increment objective, virtual speed objective, and overshoot objective for the discretized state-space model of the VSG, and designing specified constraints, the specified constraints include: ; ; ; ; ; in, , , These are the minimum allowable values ​​for each control parameter; , , These are the minimum allowable values ​​for each control parameter.

[0038] S104, Solve the multi-objective optimization function to obtain the optimal control reference values ​​for the VSG control layer, including: reference values ​​for the change in moment of inertia. Reference value of damping coefficient change Reference value of the change in proportional-integral coefficient of reactive power control loop .

[0039] The third and fourth objective functions conflict, and a Pareto optimization problem can be constructed using the gradient descent method for joint optimization. Specifically, in step S104 of this embodiment, before solving the multi-objective optimization function, the relative weights between the third and fourth objective functions are optimized, including: constructing a Pareto optimization problem for the third and fourth objective functions as shown in the following equation: ; in, For weight parameters, The third objective function corresponding to the virtual rotational speed target. Let the fourth objective function be the overshoot objective. Let be the independent variable of the third objective function. Let be the independent variable of the fourth objective function. The gradient operator; for Pareto optimization problems, the gradient solution method is used to find the weight parameters of the second objective function. Find the weight parameters within the interval [0,1]. The optimal value is then determined, and the total weights allocated between the third and fourth objective functions are based on the weight parameters. The optimal values ​​are redistributed to the weights. This embodiment combines adaptive dynamic adjustment of control parameters with the Pareto front, which can solve multi-objective optimization problems with objectives such as system dynamic response overshoot and response speed. It also uses the optimal stability condition of the second-order system as the constraint condition of the control parameters, ensuring that the MMC can quickly track changes in the system's active and reactive power commands, significantly improving the system's dynamic response performance and grid-connected stability.

[0040] In this embodiment, the multi-objective optimization function is composed of a first objective function corresponding to the power tracking objective, a second objective function corresponding to the parameter increment objective, a third objective function corresponding to the virtual speed objective, and a fourth objective function corresponding to the overshoot objective. Specifically, the multi-objective optimization function is composed of the first objective function corresponding to the power tracking objective, the second objective function corresponding to the parameter increment objective, the third objective function corresponding to the virtual speed objective, and the fourth objective function corresponding to the overshoot objective, which are weighted and summed by preset weight parameters.

[0041] Step 2, after using a Model Predictive Controller (MPC) to perform online collaborative optimization and adjustment of the control parameters of the VSG control layer to obtain the control reference value of the VSG control layer, also includes: generating a target voltage reference value based on the control reference value through the VSG control layer. Based on the target voltage reference, through the MMC control layer Power injection into a modular multilevel converter, such as... Figure 2 As shown.

[0042] The control system in this embodiment consists of two parts: an upper VSG control layer and a lower MMC control layer.

[0043] (1) The VSG control layer is based on the model predictive controller (MPC), which controls the moment of inertia J and damping coefficient of the active control loop. and reactive power control proportional coefficient Control parameters are adjusted and optimized online in a coordinated manner.

[0044] The VSG control layer adopts a typical VSG structure, including an active-frequency control loop and a reactive-frequency control loop: the active-frequency control loop is based on the active power command. Calculate the virtual power deviation with the grid frequency deviation, and combine it with parameters. and Real-time adjustment of the power system's kinetic response capability; in the reactive-frequency control loop, through reactive power deviation and the introduction of parameters. It can dynamically correct reactive power regulation capabilities, thereby improving the voltage support performance of the power system.

[0045] The VSG control layer ultimately outputs the MMC target voltage reference. For use in subsequent power injection control.

[0046] like Figure 2 As shown, the functional expression of the active-frequency control loop is: ; The functional expression for the reactive-voltage control loop is: ; in, This is the no-load electromotive force.

[0047] It should be noted that the target voltage reference is generated by the VSG control layer based on the control reference value. This is a known existing method; for details, please refer to the following literature: Yang Yun, Mei Fei, Zhang Chenyu, et al. Cooperative adaptive control strategy for moment of inertia and damping coefficient of virtual synchronous generator [J]. Electric Power Automation Equipment, 2019(3):7. DOI:10.16081 / j.issn.1006-6047.2019.03.020.

[0048] (2) The MMC control layer is based on a two-level current controller to achieve accurate current tracking.

[0049] Among them, the MMC interphase current suppression control is used to regulate the common-mode voltage. Differential mode current control, based on the VSG layer generated. The target current is calculated by combining the line parameters L and R. This leads to the differential voltage used for regulation. Finally, a trigger pulse signal is generated through NLM to achieve stable grid-connected operation.

[0050] like Figure 2 As shown, in this embodiment, the MMC control layer is based on the target voltage reference. Power injection into a modular multilevel converter includes: referencing the target voltage. The reference value of the output current is obtained by using the electromagnetic equation model of VSG along with the grid connection point voltage U. The reference value of the output current As the input for the MMC inner loop current control of the MMC control layer, the differential mode voltage is obtained. Differential mode voltage The common-mode voltage obtained by suppressing the interphase circulating current of the MMC control layer. Summing them together yields the lower arm voltage reference value. Differential mode voltage The common-mode voltage obtained by suppressing the interphase circulating current of the MMC control layer. Subtract to obtain the upper arm voltage reference value The lower bridge arm voltage reference value and upper arm voltage reference value The pulses generated by NLM control and submodule voltage equalization control are used to control the switching on and off of the IGBTs in the modular multilevel converter.

[0051] To verify the performance of the method in this embodiment, a simulation comparison was performed between the VSG control layer using the method of this embodiment and using fixed control parameters. The results are as follows: Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown.

[0052] Figure 3 The following are simulation comparisons of the method in this embodiment with fixed control parameters, where (a) shows the comparison of the moment of inertia J of the active control loop, and (b) shows the damping coefficient. In comparison, (c) represents the proportional-integral coefficient of the reactive power control loop. In comparison, J0, D0, and K0 represent the moment of inertia J, damping coefficient J, and damping coefficient of the active control loop under fixed control parameters, respectively. and the proportional-integral coefficient of the reactive power control loop J_mpc, D_mpc, and K_mpc represent the moment of inertia J, damping coefficient D, and K_mpc of the active control loop under the method of this embodiment, respectively. and the proportional-integral coefficient of the reactive power control loop See also Figure 3 It can be seen that when the active power command and reactive power command undergo a step change at 1 second, the moment of inertia J and damping coefficient of the active power control loop obtained by the method in this embodiment are... and the proportional-integral coefficient of the reactive power control loop All of these will change accordingly to achieve the effects of frequency stability, fast active power tracking, and minimal overshoot in the objective function.

[0053] Figure 4 This is a simulation comparison of the step active power obtained using the method of this embodiment and the method with fixed control parameters, where P_mpc, P_constant, and P_ref represent the active power obtained using the method of this embodiment, the active power obtained using the fixed control parameters, and the reference active power, respectively. See also... Figure 4 It can be seen that when the moment of inertia J and damping coefficient of the active control loop in the method of this embodiment are... and the proportional-integral coefficient of the reactive power control loop By adapting to changes and after tuning with a Pareto optimal control strategy, active power overshoot can be significantly reduced while ensuring rapid active power tracking.

[0054] Figure 5 This is a simulation comparison of the step reactive power obtained using the method of this embodiment and the method with fixed control parameters, where Q_mpc, Q_constant, and Q_ref represent the reactive power obtained using the method of this embodiment, the reactive power obtained using the fixed control parameters, and the reference reactive power, respectively. (See also...) Figure 5 It can be seen that when the moment of inertia J and damping coefficient of the active control loop in the method of this embodiment are... and the proportional-integral coefficient of the reactive power control loop After adaptive changes occur, the reactive power tracking response speed is faster, and the control effect is better.

[0055] Figure 6 This is a simulation comparison of frequency fluctuations between the method of this embodiment (variable control parameters) and the existing method with fixed control parameters, where f0 represents the frequency fluctuation of the existing method with fixed control parameters, and f_mpc represents the frequency fluctuation of the method of this embodiment (variable control parameters). See also Figure 6As can be seen from the simulation, the active power command jumps from 5MW to 15MW in 2s, while the reactive power command remains constant at 0. The simulation results show that, compared to fixed control parameters, the method in this embodiment (variable control parameters) exhibits smaller frequency fluctuations and greater stability while ensuring rapid active power tracking and significantly reducing active power overshoot.

[0056] In summary, this embodiment achieves key parameter optimization under the Model Predictive Controller (MPC). , , Collaborative optimization is performed. Unlike traditional methods that fix or empirically set VSG parameters, this invention constructs a multi-objective optimization function including virtual speed response speed and system overshoot, and combines this with a Pareto optimal strategy to resolve conflicting objectives. Simultaneously, through second-order system small-signal analysis, it establishes coupling relationships and constraint boundaries between parameters, ensuring that the controller always operates within the system's stable operating domain on the multi-objective optimization path. This method significantly improves the fault tolerance and control accuracy of grid-connected inverter systems, making it suitable for the promotion and implementation of VSG control strategies in high-proportion renewable energy grid-connected scenarios. This embodiment combines the MMC current inner loop and interphase circulating current suppression control to construct a complete MMC control structure. The voltage reference output from the VSG control layer is further calculated to obtain the line reference current, which is then input to the MMC inner loop control to obtain the differential-mode voltage. The MMC control section maintains a high-speed dynamic response, ensuring the accurate execution of the VSG control results, thereby supporting system voltage stability.

[0057] Example 2 The MMC parameter collaborative optimization control system based on grid-connected inverters provided in this embodiment includes: The control reference optimization module is configured to: acquire system model parameters and grid-type inverter control parameters; establish and discretize the state space model of the grid-type inverter to obtain the discretized state space model of the grid-type inverter; construct a multi-objective optimization function for the discretized state space model of the grid-type inverter, consisting of a weighted sum of power tracking target, parameter increment target, virtual speed target, and overshoot target; solve the multi-objective optimization function to obtain the optimal control reference value of the grid-type inverter control layer; The control module is configured to: generate a target voltage reference for the grid-type inverter control layer based on the control reference value; and inject power into the modular multilevel converter based on the target voltage reference.

[0058] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation processes are the same, so they will not be repeated here.

[0059] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the MMC parameter collaborative optimization control method based on a grid-type inverter as described in Embodiment 1 above.

[0060] Example 4 This embodiment provides a computer device, such as... Figure 7 As shown, the system includes a computer-readable storage medium 1003, a processor 1001, a communication interface 1002, and a computer program stored on the computer-readable storage medium 1003 and executable on the processor 1001. The processor 1001, communication interface 1002, and computer-readable storage medium 1003 can be connected via a bus or other means. The communication interface 1002 is used to receive and send data. When the processor 1001 executes the program, it implements the steps in the MMC parameter collaborative optimization control method based on a grid-type inverter as described in Embodiment 1 above.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for parameter collaborative optimization control of a modular multilevel converter (MMC) based on a networked inverter, characterized in that, include: Obtain system model parameters and grid-type inverter control parameters, establish and discretize the state space model of the grid-type inverter to obtain the discretized state space model of the grid-type inverter, construct a multi-objective optimization function for the discretized state space model of the grid-type inverter consisting of a power tracking objective, a parameter increment objective, a virtual speed objective, and an overshoot objective, solve the multi-objective optimization function to obtain the optimal control reference value of the grid-type inverter control layer; Based on the control reference value, the grid-type inverter control layer generates a target voltage reference, and based on the target voltage reference, the MMC control layer injects power into the modular multilevel converter.

2. The MMC parameter collaborative optimization control method based on a network-forming inverter according to claim 1, characterized in that, The multi-objective optimization function includes: ； ； ； ； in, Step size, for Output active power at any time To output active power reference value, , and For weight parameters, , , and They are respectively Moment , , and , , , and They are respectively , , and The increment, Where J is the virtual rotor speed, and J is the moment of inertia of the active control loop. The damping coefficient is... The proportional-integral coefficient of the reactive power control loop. for The overshoot of the second-order system at time t.

3. The MMC parameter collaborative optimization control method based on a grid-connected inverter as described in claim 1, characterized in that, Before solving the multi-objective optimization function, for conflicting objective functions, a Pareto optimization problem is constructed using the gradient descent method for joint optimization.

4. The MMC parameter collaborative optimization control method based on a grid-connected inverter as described in claim 1, characterized in that, When solving a multi-objective optimization function, the following constraints are set: ； ； ； ； ； in, These are the moment of inertia J of the active control loop and the damping coefficient, respectively. Proportional-integral coefficient of reactive power control loop The initial value; , and These are the minimum allowable values ​​for each control parameter; , and These are the minimum allowable values ​​for each control parameter; , and They are respectively Moment , and , , and They are respectively , and The increment, It is an intermediate variable.

5. The MMC parameter collaborative optimization control method based on a grid-connected inverter as described in claim 1, characterized in that, The system model parameters include grid-side line resistance and reactance, output active power, reactive power, active power command, reactive power command, and grid connection point voltage; The control parameters of the grid-type inverter include the port voltage and phase angle of the MMC output, as well as the virtual rotor speed of the grid-type inverter, the moment of inertia of the active control loop, the damping coefficient, and the proportional-integral coefficient of the reactive control loop. The control reference values ​​of the grid-type inverter control layer include reference values ​​for changes in moment of inertia, changes in damping coefficient, and changes in the proportional-integral coefficient of the reactive power control loop.

6. The MMC parameter collaborative optimization control method based on a grid-connected inverter as described in claim 1, characterized in that, The grid-type inverter control layer includes an active-frequency control loop and a reactive-frequency control loop.

7. The MMC parameter collaborative optimization control method based on a grid-connected inverter as described in claim 1, characterized in that, The MMC control layer includes: obtaining a reference value for the output current by using the target voltage reference and the grid connection point voltage through an electromagnetic equation model; using the reference value for the output current as the input for the MMC inner loop current control to obtain the differential mode voltage; adding the differential mode voltage and the common mode voltage obtained by MMC interphase circulating current suppression to obtain the lower arm voltage reference value; subtracting the differential mode voltage and the common mode voltage obtained by MMC interphase circulating current suppression to obtain the upper arm voltage reference value; and generating pulses by using the lower arm voltage reference value and the upper arm voltage reference value through NLM control and submodule voltage equalization control to control the switching of IGBTs in the modular multilevel converter.

8. A collaborative optimization control system for MMC parameters based on a grid-connected inverter, characterized in that, include: The control reference optimization module is configured to: acquire system model parameters and grid-type inverter control parameters; establish and discretize the state space model of the grid-type inverter to obtain the discretized state space model of the grid-type inverter; construct a multi-objective optimization function for the discretized state space model of the grid-type inverter, consisting of a weighted sum of power tracking target, parameter increment target, virtual speed target, and overshoot target; solve the multi-objective optimization function to obtain the optimal control reference value of the grid-type inverter control layer; The control module is configured to: generate a target voltage reference for the grid-type inverter control layer based on the control reference value; and inject power into the modular multilevel converter based on the target voltage reference.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the MMC parameter collaborative optimization control method based on a grid-type inverter as described in any one of claims 1-7.

10. A computer device comprising a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the MMC parameter collaborative optimization control method based on a grid-type inverter as described in any one of claims 1-7.

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