Two-stage new energy grid-connected inverter system parallel distributed compensation method

Abstract

The application discloses a two-stage new energy grid-connected inverter system parallel distributed compensation method, comprising: establishing a nonlinear large signal model of a grid-connected inverter system under a synchronous control scale; processing nonlinear terms in the nonlinear large signal model, and extracting a plurality of nonlinear terms; constructing corresponding membership functions according to upper and lower boundaries of the nonlinear terms, and establishing a T-S fuzzy equivalent model of the grid-connected inverter system; designing a parallel distributed compensation controller based on each linear subsystem in the T-S fuzzy equivalent model, obtaining a fuzzy state feedback control law; and applying the fuzzy state feedback control law to a voltage outer loop control circuit of the grid-connected inverter system, and implementing nonlinear feedback compensation on the grid-connected inverter system. The application can accurately characterize double-loop coupling characteristics, effectively expand a system transient stability domain, and significantly improve stable operation ability of the system under large disturbance.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation control, specifically to a parallel distributed compensation method for a two-stage new energy grid-connected inverter system. Background Technology

[0002] As environmental pollution and energy shortages caused by traditional energy sources become increasingly prominent, the global energy structure transformation is accelerating, and renewable energy power generation technologies are being vigorously developed. The construction of new power systems composed of renewable energy sources such as hydropower, wind power, and photovoltaics is in full swing. Among these, the two-stage grid-connected inverter system, as the core equipment for connecting new energy power generation to the grid, directly affects the safe grid connection and efficient utilization of new energy power.

[0003] The synchronous control scale of a two-stage grid-connected inverter system carries both fast and slow-scale dynamic changes and is a key aspect of system stability analysis. At this scale, a complex nonlinear coupling relationship exists between the DC-side voltage outer loop and the phase-locked loop (PLL), which makes the system prone to transient instability under large disturbances. Current optimization control methods for the transient stability of two-stage grid-connected inverter systems exhibit limited robustness and adaptability under complex operating conditions among traditional linear control strategies; intelligent control methods such as neural network control rely on large-scale training data, resulting in high computational complexity and difficulty in ensuring real-time performance; while existing parallel distributed compensation (PDC) control, although possessing advantages in nonlinear processing, suffers from challenges in designing feedback matrices and slow controller response speeds in scenarios with multiple fuzzy rules, failing to fully meet the transient stability control requirements of two-stage grid-connected inverter systems.

[0004] Therefore, in order to solve the problems of poor transient stability caused by dual-loop coupling under the synchronous control scale of existing two-stage new energy grid-connected inverter systems, and the complex design and limited effectiveness of compensation methods, it is urgent to propose a parallel distributed compensation method that can accurately characterize the dual-loop coupling characteristics, effectively expand the transient stability domain of the system, and has strong engineering applicability. Summary of the Invention

[0005] In view of this, the purpose of this invention is to overcome the defects in the prior art and provide a parallel distributed compensation method for a two-stage new energy grid-connected inverter system, which can accurately characterize the dual-ring coupling characteristics, effectively expand the transient stability domain of the system, and significantly improve the system's stable operation capability under large disturbances.

[0006] The parallel distributed compensation method for a two-stage renewable energy grid-connected inverter system of the present invention includes:

[0007] A nonlinear large-signal model of the grid-connected inverter system under synchronous control is established. The nonlinear large-signal model is used to characterize the dynamic coupling relationship between the DC voltage outer loop and the phase-locked loop.

[0008] The nonlinear terms in the nonlinear large-signal model are processed to extract multiple nonlinear terms and determine the upper and lower boundaries of each nonlinear term.

[0009] Based on the upper and lower boundaries of the nonlinear terms, the corresponding membership functions are constructed, and each nonlinear term is expressed as a weighted combination of its boundary values ​​to establish the TS fuzzy equivalent model of the grid-connected inverter system.

[0010] Based on each linear subsystem in the TS fuzzy equivalent model, a parallel distributed compensation controller is designed. By solving the state feedback control law corresponding to each linear subsystem, and performing fuzzy weighted fusion of each state feedback control law according to the membership function, the fuzzy state feedback control law is obtained.

[0011] The fuzzy state feedback control law is applied to the voltage outer loop control circuit of the grid-connected inverter system to implement nonlinear feedback compensation for the grid-connected inverter system.

[0012] Furthermore, the state variables of the nonlinear large-signal model , , , for:

[0013] , , , ;

[0014] in, The phase difference of the phase-locked loop. Let be the integral state variable of the phase-locked loop. This is the DC bus voltage. For the voltage outer loop output Shaft reference current;

[0015] The dynamics of the nonlinear large-signal model are described by the following set of state equations:

[0016] ;

[0017] in, This is the derivative of the phase difference between the phase-locked loop and the power grid with respect to time; For the phase-locked loop controller, the integral of the state variable with respect to time is the derivative. This is the derivative of the DC bus voltage with respect to time. For the voltage outer loop output The derivative of the shaft reference current with respect to time; The proportional gain of the phase-locked loop; For the equivalent inductive reactance of the power grid; This refers to the voltage amplitude of the power grid. The fundamental angular frequency of the grid voltage; The integral coefficients of the phase-locked loop; This is the input power of the preamplifier; For DC-side support capacitors; This is the proportional coefficient of the outer voltage loop; The given value for DC voltage; is the integral coefficient of the outer voltage loop.

[0018] Furthermore, the nonlinear term includes , , , as well as ;

[0019] The nonlinear term is determined according to the following formula:

[0020] ;

[0021] in, The proportional gain of the phase-locked loop; This is the equivalent inductive reactance of the power grid.

[0022] Furthermore, the TS fuzzy equivalent model is determined according to the following formula:

[0023] ;

[0024] in, This is the derivative of the phase difference between the phase-locked loop and the power grid with respect to time; For the phase-locked loop controller, the integral of the state variable with respect to time is the derivative. This is the derivative of the DC bus voltage with respect to time. For the voltage outer loop output The derivative of the shaft reference current with respect to time; For the corresponding to the first The overall membership function of a fuzzy rule is composed of the product of the membership degrees of each nonlinear term; For the first The state matrix of a linear subsystem; The input vector; The input matrix of the system; The input vector; ; This is the DC voltage setpoint.

[0025] Furthermore, the output control quantity of the parallel distributed compensation controller Calculated by the following formula:

[0026] ;

[0027] in, For the state matrix The corresponding state feedback gain matrix; The number of subsystems.

[0028] Furthermore, nonlinear feedback compensation is implemented on the grid-connected inverter system, specifically including:

[0029] The output of the parallel distributed compensation controller The output of the original voltage outer loop PI controller is superimposed and compensated to jointly generate the d-axis reference current command of the current inner loop. :

[0030] ;

[0031] in, This is the proportional coefficient of the outer voltage loop; This is the DC bus voltage; The given value for DC voltage; The integral coefficient of the outer voltage loop; The input vector; It is a time variable.

[0032] Furthermore, it also includes: designing faster-responding reduced-order parallel distributed compensation controllers for dynamics dominated by the voltage outer loop and dynamics dominated by the phase-locked loop, respectively.

[0033] Furthermore, a reduced-order parallel distributed compensation controller is designed for dynamics dominated by the voltage outer loop, specifically including: ignoring phase-locked loop-related state variables. and DC bus voltage The dynamic response of the phase-locked loop and the related state variables. and DC bus voltage The blur range is reduced, making its blur range infinitely close to zero.

[0034] Furthermore, for phase-locked loop-dominated dynamic design, a reduced-order parallel distributed compensation controller is designed, specifically including: incorporating voltage outer loop-related state variables... and The fuzzy range compression simplifies the nonlinear term to only those related to... and Related.

[0035] The beneficial effects of this invention are as follows: The parallel distributed compensation method for a two-stage renewable energy grid-connected inverter system disclosed in this invention, based on a large-signal model considering the coupling relationship between the voltage outer loop and the phase-locked loop, designs a parallel distributed compensation controller for the TS fuzzy model, decomposes the complex nonlinear system into multiple local linear subsystems, designs state feedback control in each subsystem and combines it through fuzzy inference weighted combination, thereby realizing the coordinated nonlinear regulation of the voltage outer loop and the phase-locked loop, thus effectively expanding the transient stability domain of the system and significantly improving the system's stable operation capability under large disturbances. Attached Figure Description

[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0037] Figure 1 This is a schematic diagram of PDC control in an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram illustrating the reduction of the dynamic response of PDC x1 and x3 in an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram illustrating the reduction of the x4 and x3 dynamic responses of PDC in an embodiment of the present invention;

[0040] Figure 4 This is a simulation verification diagram of the original large signal model and the TS fuzzy model in an embodiment of the present invention;

[0041] Figure 5 This is a comparison diagram of the attraction domains of the original system and the PDC system x4 in this embodiment of the invention;

[0042] Figure 6 This is a comparison diagram of the attraction domains of the original system and the PDC system x1 in this embodiment of the invention;

[0043] Figure 7 The figure shows the simulation results of PDC optimization for voltage outer loop disturbance in the original system and PDC system in this embodiment of the invention.

[0044] Figure 8 The figure shows the simulation results of PDC optimization for the original system and the PDC system phase-locked loop disturbance in the embodiment of the present invention. Detailed Implementation

[0045] The present invention will be further described below with reference to the accompanying drawings, as shown in the figures:

[0046] This embodiment discloses a parallel distributed compensation method for a two-stage renewable energy grid-connected inverter system, including the following steps:

[0047] S1. Establish a nonlinear large-signal model of the grid-connected inverter system under synchronous control scale. The nonlinear large-signal model is used to characterize the dynamic coupling relationship between the DC voltage outer loop and the phase-locked loop.

[0048] S2. Process the nonlinear terms in the nonlinear large-signal model, extract multiple nonlinear terms, and determine the upper and lower boundaries of each nonlinear term;

[0049] S3. Construct the corresponding membership function based on the upper and lower boundaries of the nonlinear terms, express each nonlinear term as a weighted combination of its boundary values, and establish the TS fuzzy equivalent model of the grid-connected inverter system.

[0050] S4. Based on each linear subsystem in the TS fuzzy equivalent model, a parallel distributed compensation controller is designed. By solving the state feedback control law corresponding to each linear subsystem, and performing fuzzy weighted fusion of each state feedback control law according to the membership function, the fuzzy state feedback control law is obtained.

[0051] S5. Apply the fuzzy state feedback control law to the voltage outer loop control circuit of the grid-connected inverter system to implement nonlinear feedback compensation for the grid-connected inverter system.

[0052] This invention establishes an accurate fuzzy equivalent model of the voltage series circuit (TS) and designs a targeted parallel distributed compensation strategy to achieve coordinated nonlinear control of the voltage outer loop and the phase-locked loop, thereby effectively expanding the transient stability domain of the system and improving the system's stable operation capability under large disturbances.

[0053] In this embodiment, in step S1, a two-stage new energy grid-connected inverter system is taken as the research object. The dynamics of the inner current loop are ignored, and only the dynamic coupling of the outer voltage loop and the phase-locked loop is considered.

[0054] Set state variables , , , for:

[0055] , , , ;

[0056] in, The phase difference of the phase-locked loop. Let be the integral state variable of the phase-locked loop. This is the DC bus voltage. For the voltage outer loop output Shaft reference current;

[0057] Based on power balance and phase-locked loop dynamics, a nonlinear large-signal model is established, yielding the following set of state equations:

[0058] ;

[0059] in, This is the derivative of the phase difference between the phase-locked loop and the power grid with respect to time; For the phase-locked loop controller, the integral of the state variable with respect to time is the derivative. This is the derivative of the DC bus voltage with respect to time. For the voltage outer loop output The derivative of the shaft reference current with respect to time; The proportional gain of the phase-locked loop; For the equivalent inductive reactance of the power grid; This refers to the voltage amplitude of the power grid. The fundamental angular frequency of the grid voltage; The integral coefficients of the phase-locked loop; This is the input power of the preamplifier; For DC-side support capacitors; This is the proportional coefficient of the outer voltage loop; The given value for DC voltage; is the integral coefficient of the outer voltage loop.

[0060] In this embodiment, in step S2, based on the sector interval method, the nonlinear terms in the nonlinear large signal model are subjected to fuzzy linearization processing, key nonlinear terms are extracted and their fuzzy boundaries are determined.

[0061] The five key nonlinear terms extracted from the nonlinear large-signal model are as follows: , , , as well as The nonlinear term is determined according to the following formula:

[0062] ;

[0063] in, The proportional gain of the phase-locked loop; This is the equivalent inductive reactance of the power grid.

[0064] Based on the actual operating range of the system, a fuzzy range is selected. ∊[-a,a], ∊[-b,b], ∊[-c,c], using the sector interval method to determine the upper and lower bounds of each nonlinear term, thus representing it as a weighted sum of two linear extrema. For example, for : ; ;right Perform fuzzy linearization, let: ;in for The membership function can be expressed as:

[0065] ; .

[0066] In this embodiment, in step S3, based on the processing result of step S2, a TS fuzzy equivalent model of the system is constructed. This model is represented as a fuzzy weighted combination of multiple linear subsystems.

[0067] Specifically, based on the membership functions of the five nonlinear terms, the overall membership function of each fuzzy rule is calculated. Since each nonlinear term has two extreme cases, we can obtain... There are 32 fuzzy rules, each corresponding to a linear subsystem. Therefore, the large-signal model of the system can be represented as a fuzzy combination of 32 linear subsystems, with each subsystem having a state matrix. Substitute the extreme values ​​of each nonlinear term into the original system matrix. We obtain the matrix. Constructed from nonlinear terms, for example The form will not be elaborated here.

[0068] The final form of the TS fuzzy equivalent model is:

[0069] ;

[0070] in, This is the derivative of the phase difference between the phase-locked loop and the power grid with respect to time; For the phase-locked loop controller, the integral of the state variable with respect to time is the derivative. This is the derivative of the DC bus voltage with respect to time. For the voltage outer loop output The derivative of the shaft reference current with respect to time; For the corresponding to the first The overall membership function of a fuzzy rule is composed of the product of the membership degrees of each nonlinear term; For the first The state matrix of a linear subsystem; The input vector; The input matrix of the system; The input vector; ; This is the DC voltage setpoint.

[0071] In this embodiment, in step S4, a parallel distributed compensation controller is designed for the TS fuzzy equivalent model established in step S3.

[0072] Specifically, for each of the 32 fuzzy rules, 32 corresponding state feedback gain matrices are designed. The controller design follows the principle of "parallel distributed compensation," meaning that the controller for each fuzzy rule shares the same premise with its corresponding fuzzy model rule.

[0073] The output control quantity of the parallel distributed compensation controller The final output is calculated using the following formula:

[0074] ;

[0075] in, For the state matrix The corresponding state feedback gain matrix; Number of subsystems. Feedback gain matrix. The desired closed-loop poles can be configured or the linear matrix inequality (LMI) can be solved to ensure the stability and performance of each local linear subsystem.

[0076] In this embodiment, in step S5, the controller designed in step S4 is integrated into the grid-connected inverter system; specifically, the output of the parallel distributed compensation controller is integrated into the grid-connected inverter system. This is introduced into the original large-signal system modeling. The addition of the PDC controller only changes the state variables. The expression is given. After reverse-engineering the coefficient matrix, it is found that to derive the TS fuzzy equivalent model after adding the PDC controller, the state variables need to be changed. , so that:

[0077] (1)

[0078] in, This is the proportional coefficient of the outer voltage loop; This is the DC bus voltage; The given value for DC voltage; The integral coefficient of the outer voltage loop; The input vector; It is a time variable.

[0079] The formula derivation shows that the addition of the PDC controller to the TS fuzzy model will change the state variables. The differential equation, and The control loop output is obtained after the DC side voltage value is collected and controlled by the voltage loop. Therefore, it can be analyzed that the PDC controller needs to be added to the outer voltage loop to change the voltage loop structure in order to obtain the equivalent TS fuzzy model corresponding to formula (1). Among them, according to formula (1), the schematic diagram of the position of the PDC controller added to the voltage loop is shown in the figure. Figure 1 As shown.

[0080] That is: the output of the parallel distributed compensation controller The output of the original voltage outer loop PI controller is superimposed and compensated to jointly generate the d-axis reference current command of the current inner loop. .

[0081] In this embodiment, considering that 32 fuzzy rules may lead to complex controller design and slow response speed, the present invention further reduces the order of the designed PDC. That is, it also includes: designing a faster-responding reduced-order parallel distributed compensation controller for the dynamics dominated by the voltage outer loop and the dynamics dominated by the phase-locked loop, respectively.

[0082] For the TS fuzzy model of this system, the system has five nonlinear terms, which form a model with 32 fuzzy rules. The five nonlinear terms are derived from three state variables. , , The fuzzy range of the TS fuzzy model is determined by the range of the fuzzy range.

[0083] However, regarding the state variables in the voltage outer loop When designing a PDC controller, ignore... and The dynamic response, specifically, the design of a reduced-order parallel distributed compensation controller for the dynamics dominated by the voltage outer loop, includes ignoring the state variables related to the phase-locked loop. and DC bus voltage The dynamic response of the phase-locked loop and the related state variables. and DC bus voltage The blur range is reduced, making its blur range infinitely close to zero.

[0084] Among them, reducing and A schematic diagram of the dynamic response is shown below. Figure 2 As shown, Figure 2 Nonlinear terms and The dynamic response including phase-locked loop. , , and The dynamic response including the voltage loop, with the TS fuzzy model and The compression of the fuzzy range transforms the original TS fuzzy model, which considered the dynamic response of five nonlinear terms, into one that only considers... , In the dynamically responding TS fuzzy model, the 32 linear subspaces are compressed into 4 large groups of linear subspaces. Each large group of linear subspaces contains eight identical smaller linear subspaces. The feedback matrices of these subspaces are roughly the same, thus making the feedback matrix... The number of combinations is greatly reduced.

[0085] In addition, for phase-locked loop-dominated dynamic design of reduced-order parallel distributed compensation controller, specifically including: the voltage outer loop related state variables and The fuzzy range compression simplifies the nonlinear term to only those related to... and Relatedly, this also simplifies the rules.

[0086] Among them, reducing and Dynamic response diagram as follows Figure 3 As shown. The feedback matrix after order reduction. The essence of design is the selection of eigenvalues. After selecting appropriate eigenvalues, they are substituted into the coefficient matrix and feedback matrix. After obtaining the eigenvalues, we can deduce... =The specific value of [ABCD].

[0087] Furthermore, the simulation analysis is explained as follows:

[0088] A simulation model of a 50kW two-stage grid-connected inverter system was built using Matlab / Simulink, and the simulation results are shown in Table 1.

[0089] Table 1

[0090]

[0091] The feedback matrix of the PDC controller was designed based on the voltage outer loop state variables of the 50kW simulation system. :

[0092] =[0.508,-0.42,31.7,-0.35];

[0093] =[0.5,-0.35,20.55,-0.304];

[0094] =[0.55,-0.36,31.9,-0.345];

[0095] =[0.501,-0.25,20.489,-0.29].

[0096] The feedback matrix of the PDC controller designed based on the state variables of the phase-locked loop of the 50KW simulation system. :

[0097] =[1.4,-1.9,-10.14,-0.246];

[0098] =[1.7,-0.5,-10.792,-0.187];

[0099] =[1.44,-1.12,-10.13,-0.246];

[0100] =[1.36,-1.34,-10,-0.188].

[0101] Simulation results show that:

[0102] (1) The established TS fuzzy model can accurately track the dynamic response of the original large-signal model, such as Figure 4 As shown.

[0103] (2) The system attraction domain analysis shows that the PDC controller can significantly expand the voltage outer loop state variables. Phase-locked loop state variables The stability regions are as follows: Figure 5 , Figure 6 As shown.

[0104] (3) When a large disturbance is introduced into the voltage outer loop or phase-locked loop, the system using the PDC control of this invention can quickly recover stability, while the system without PDC control will become unstable, as shown below. Figure 7 , Figure 8 As shown, the effectiveness and engineering feasibility of the present invention have been verified.

[0105] The method of this invention has the advantages of accurate modeling, flexible control, strong stability, and good engineering applicability, and is suitable for the stable control of grid-connected systems of new energy sources such as photovoltaic and wind power.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A parallel distributed compensation method for a two-stage renewable energy grid-connected inverter system, characterized in that: include: A nonlinear large-signal model of the grid-connected inverter system under synchronous control is established. The nonlinear large-signal model is used to characterize the dynamic coupling relationship between the DC voltage outer loop and the phase-locked loop. The nonlinear terms in the nonlinear large-signal model are processed to extract multiple nonlinear terms and determine the upper and lower boundaries of each nonlinear term. Based on the upper and lower boundaries of the nonlinear terms, the corresponding membership functions are constructed, and each nonlinear term is expressed as a weighted combination of its boundary values ​​to establish the TS fuzzy equivalent model of the grid-connected inverter system. Based on each linear subsystem in the TS fuzzy equivalent model, a parallel distributed compensation controller is designed. By solving the state feedback control law corresponding to each linear subsystem, and performing fuzzy weighted fusion of each state feedback control law according to the membership function, the fuzzy state feedback control law is obtained. The fuzzy state feedback control law is applied to the voltage outer loop control circuit of the grid-connected inverter system to implement nonlinear feedback compensation for the grid-connected inverter system.

2. The parallel distributed compensation method for a two-stage renewable energy grid-connected inverter system according to claim 1, characterized in that: The state variables of the nonlinear large-signal model , , , for: 、 、 、 ； in, The phase difference of the phase-locked loop. Let be the integral state variable of the phase-locked loop. This is the DC bus voltage. For the voltage outer loop output Shaft reference current; The dynamics of the nonlinear large-signal model are described by the following set of state equations: ； in, This is the derivative of the phase difference between the phase-locked loop and the power grid with respect to time; For the phase-locked loop controller, the integral of the state variable with respect to time is the derivative. This is the derivative of the DC bus voltage with respect to time. For the voltage outer loop output The derivative of the shaft reference current with respect to time; The proportional gain of the phase-locked loop; For the equivalent inductive reactance of the power grid; This refers to the voltage amplitude of the power grid. The fundamental angular frequency of the grid voltage; The integral coefficients of the phase-locked loop; This is the input power of the preamplifier; For DC-side support capacitors; This is the proportional coefficient of the outer voltage loop; The given value for DC voltage; is the integral coefficient of the outer voltage loop.

3. The parallel distributed compensation method for a two-stage renewable energy grid-connected inverter system according to claim 1, characterized in that: The nonlinear term includes , , , as well as ; The nonlinear term is determined according to the following formula: ； in, The proportional gain of the phase-locked loop; This is the equivalent inductive reactance of the power grid.

4. The parallel distributed compensation method for a two-stage renewable energy grid-connected inverter system according to claim 1, characterized in that: The TS fuzzy equivalent model is determined according to the following formula: ； in, This is the derivative of the phase difference between the phase-locked loop and the power grid with respect to time; For the phase-locked loop controller, the integral of the state variable with respect to time is the derivative. This is the derivative of the DC bus voltage with respect to time. For the voltage outer loop output The derivative of the shaft reference current with respect to time; For the corresponding to the first The overall membership function of a fuzzy rule is composed of the product of the membership degrees of each nonlinear term; For the first The state matrix of a linear subsystem; The input vector; The input matrix of the system; The input vector; ; This is the DC voltage setpoint.

5. The parallel distributed compensation method for a two-stage renewable energy grid-connected inverter system according to claim 1, characterized in that: The output control quantity of the parallel distributed compensation controller Calculated by the following formula: ； in, For the state matrix The corresponding state feedback gain matrix; The number of subsystems.

6. The parallel distributed compensation method for a two-stage renewable energy grid-connected inverter system according to claim 1, characterized in that: The nonlinear feedback compensation for the grid-connected inverter system specifically includes: The output of the parallel distributed compensation controller The output of the original voltage outer loop PI controller is superimposed and compensated to jointly generate the d-axis reference current command of the current inner loop. : ； in, This is the proportional coefficient of the outer voltage loop; This is the DC bus voltage; The given value for DC voltage; The integral coefficient of the outer voltage loop; The input vector; It is a time variable.

7. The parallel distributed compensation method for a two-stage renewable energy grid-connected inverter system according to claim 1, characterized in that: Also includes: We designed a faster-responding reduced-order parallel distributed compensation controller for dynamics dominated by the voltage outer loop and dynamics dominated by the phase-locked loop, respectively.

8. The parallel distributed compensation method for a two-stage renewable energy grid-connected inverter system according to claim 7, characterized in that: Design a reduced-order parallel distributed compensation controller for dynamics dominated by the voltage outer loop, specifically including: ignoring phase-locked loop-related state variables. and DC bus voltage The dynamic response of the phase-locked loop and the related state variables. and DC bus voltage The blur range is reduced, making its blur range infinitely close to zero.

9. The parallel distributed compensation method for a two-stage renewable energy grid-connected inverter system according to claim 7, characterized in that: Designing a reduced-order parallel distributed compensation controller for phase-locked loop-dominated dynamic systems specifically includes: incorporating voltage outer loop-related state variables... and The fuzzy range compression simplifies the nonlinear term to only those related to... and Related.

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