A network construction-following network fusion grid connection control stability optimization method and device suitable for grid strength characteristics

Abstract

The present disclosure relates to a network-construction-following-network fusion and grid-connection control stability optimization method and device suitable for grid strength characteristics. The method comprises: establishing a small signal state space model of an inverter network-construction-following-network fusion control system; analyzing the stability of the system under different grid strengths and different fusion coefficients; solving the optimal fusion coefficient that minimizes the real part of the dominant eigenvalue of the system and applying it to the controller, with the optimal system stability as the target and the system stability as the constraint. The network-construction-following-network fusion controller weights and fuses the network-construction angle frequency controlled by the virtual synchronous machine and the network-following angle frequency controlled by the phase-locked loop through the fusion coefficient to obtain the system angle frequency. The present application can adapt to changes in grid strength without switching control modes, avoiding transient impact current; only the fusion coefficient needs to be adjusted without modifying the original controller parameters; the optimal fusion coefficient is solved by using a particle swarm optimization method based on a small habitat, ensuring global optimization and significantly improving the system stability margin.

Description

Technical Field

[0001] This disclosure relates to the field of grid-connected control, specifically to a method and apparatus for stability optimization of grid-connected control that adapts to the characteristics of the power grid intensity. Background Technology

[0002] Distributed energy resources are typically connected to the grid via inverters. Inverters primarily employ two control methods: grid-connected control and grid-following control. Grid-connected inverters typically use methods such as virtual synchronous machine control and droop control, which can support grid voltage and frequency stability, ensuring stable system operation even under weak grid conditions. However, under strong grid conditions, they may couple with line impedance, leading to system oscillations and instability. Conversely, grid-following inverters generally use constant power control, tracking the grid voltage and frequency. Under weak grid conditions, this may reduce system stability or even cause instability, but they can still operate stably under strong grid conditions. Before distributed energy resources are connected to the grid, the strength of the grid connection cannot be determined. In this case, relying solely on grid-connected or grid-following control may lead to decreased system stability, or even fail to guarantee stable system operation. Therefore, a control strategy needs to be developed to maximize system stability while ensuring overall system stability.

[0003] To address the issue of decreased system stability or even instability caused by distributed energy resources connected to the grid through grid-connected or grid-integrated control, current mainstream approaches include grid-connected-grid switching control, grid-connected-grid hybrid synchronization control, and grid-connected-grid parallel control. Grid-connected-grid switching control involves switching the inverter control mode according to grid strength, enabling stable system operation over a wide range of grid strength variations. Grid-connected-grid hybrid synchronization control allows the inverter to possess both grid-connected and grid-integrated characteristics, improving system stability margin. Grid-connected-grid parallel control can adjust the proportion of grid-connected control to ensure system stability under different grid strength conditions.

[0004] The grid-connection switching control method generates transient inrush currents when switching control modes, affecting the transient stability of the system. In the grid-connection hybrid synchronous control, the grid-connection control and the grid-connection control are fixed at a 1:1 weighting, which cannot adapt to the complex operating conditions of grid intensity changes. In the grid-connection parallel control, when the grid intensity changes, the proportion of grid-connection control and the parameters of each control link must change accordingly, making the design complex.

[0005] Therefore, one or more methods are needed to solve the above problems.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this disclosure is to provide a grid-connection control stability optimization method and device adapted to the grid strength characteristics, thereby overcoming at least to some extent one or more problems caused by the limitations and defects of related technologies.

[0008] According to one aspect of this disclosure, a grid-connection control stability optimization method adapted to the grid strength characteristics is provided, comprising: A small-signal state-space model of the inverter grid-connection integrated control system is established. The inverter grid-connection integrated control system includes a grid-connection integrated controller, an inverter, a load, a transformer, lines, and a power grid. The grid-connection integrated controller weights and fuses the angular frequency of the grid-connection control output with the angular frequency of the grid-connection control output using a fusion coefficient to obtain the system angular frequency. Based on the system angular frequency, a front-end voltage reference value is generated and output to the inverter after dual closed-loop control. Based on the small-signal state-space model, the stability of the inverter grid-connection control system under different grid strengths and different fusion coefficients is analyzed. With the goal of optimizing the stability of the inverter grid-connection and grid-integration control system and the constraint of the stability of the inverter grid-connection and grid-integration control system, the optimal fusion coefficient is solved, and the optimal fusion coefficient is applied to the grid-connection and grid-integration controller to realize the inverter grid-connection and grid-integration control. The optimal fusion coefficient is the fusion coefficient that minimizes the real part of the dominant eigenvalue of the inverter grid-connection and grid-integration control system.

[0009] In one exemplary embodiment of this disclosure, the method further includes: The network-type control uses virtual synchronous machine control, and the follow-network type control uses phase-locked loop control. The mesh-type control outputs the mesh-type angular frequency, and the following mesh-type control outputs the following mesh angular frequency; The system angular frequency is obtained by weighted fusion of the meshing angular frequency and the tracking angular frequency using the fusion coefficient, and the fusion formula is: , in,

[0010] In the formula: V o,q , I L , I lo , I c , ω f , I o, K pll p , K pll i These are the inverter outputs. q The integral of the shaft voltage, the filter inductor current, the load current, the line current, the cutoff angular frequency, the disturbance current, the phase-locked loop proportional coefficient, and the phase-locked loop integral coefficient.

[0011] In one exemplary embodiment of this disclosure, the method further includes a voltage loop, a current loop, and a reactive power control loop in the grid-connection controller. The small-signal model of the voltage loop is: , in,

[0012] In the formula: , , K up , K ui These are the integral of the voltage loop controller output, the setpoint of the filter inductor current, the voltage loop proportional coefficient, and the voltage loop integral coefficient, respectively. The small-signal model of the current loop is: , in,

[0013] In the formula: , , K ip , K ii These are the integral of the current loop controller output, the modulation voltage, the current loop proportional coefficient, and the current loop integral coefficient, respectively. The reactive power control loop adopts reactive power droop control, and generates a voltage amplitude reference value based on the rated reactive power, actual reactive power and rated voltage.

[0014] In one exemplary embodiment of this disclosure, the establishment of the small-signal state-space model in the method includes: Small-signal models for the grid-connected controller, voltage loop, current loop, inverter, load, and line are established separately. The inverter small-signal model is as follows: , in, ; The load small-signal model is as follows: , in,

[0015] In the formula: r load , L load These are the load resistance and the load inductance, respectively. The line small-signal model is as follows: , in, ; By combining the small-signal models, we obtain the small-signal state-space model of the overall system:

[0016] in,

[0017] In the formula: x s , u s These are the state variables and input variables in the small-signal model of the system, respectively. A s , B s These are the state matrix and input matrix in the small-signal model of the system, respectively.

[0018] In one exemplary embodiment of this disclosure, the method further includes: The overall system small-signal state-space model has 16 state variables and the state matrix is ​​a 16×16 matrix. After solving for the eigenvalues ​​of the state matrix, a zero eigenvalue was found. Participation factor analysis was used to identify the relationship between this zero eigenvalue and the state variable Δ. λ d Δ λ q Related; analysis shows that the state variable Δ λ d Δ λ q To eliminate redundant state variables, the small-signal state-space model of the overall system is reduced in order to obtain a reduced-order small-signal model of the system.

[0019] in, .

[0020] In one exemplary embodiment of this disclosure, the grid strength in the method is characterized by the short-circuit ratio, and the formula for calculating the short-circuit ratio is:

[0021] In the formula: S SC , S n , U n , Z line These are the short-circuit capacity of the inverter's grid connection node, the inverter's rated apparent power, the rated grid voltage, and the line impedance between the inverter and the grid.

[0022] In an exemplary embodiment of this disclosure, the stability analysis of the method includes: under a fixed power grid strength, changing the value of the fusion coefficient, solving the dominant eigenvalue of the small-signal state-space model, and obtaining the stability trend of the dominant eigenvalue as the fusion coefficient changes; Under strong power grid conditions, as the fusion coefficient gradually increases from 0 to 1, the trend of the real part of the dominant eigenvalue is: left shift, right shift, left shift, right shift, left shift, right shift, unchanged; Under weak grid conditions, as the fusion coefficient gradually increases from 0 to 1, the trend of the real part of the dominant eigenvalue is: left shift, right shift, or no change.

[0023] In one exemplary embodiment of this disclosure, the method further includes: The solution for the optimal fusion coefficient is to minimize the real part of the largest eigenvalue, with the constraint that the real parts of all eigenvalues ​​of the system are less than 0. The objective function is: : The constraint condition is: : in, A sj Let be the state matrix of the small-signal state-space model of the reduced-order system. The eig() function is used to find the eigenvalues, and the real() function is used to find the real part.

[0024] In one exemplary embodiment of this disclosure, the optimal fusion coefficient is solved using a niche-based particle swarm optimization method, which includes: Establish a system state-space model and obtain the steady-state operating points of voltage and current; Input the fusion coefficient, construct the system small signal model and calculate the feature value. Take minimizing the maximum real part of the dominant feature value as the objective function, set the niche particle swarm parameters, and initialize the particle swarm position and velocity. The position represents the value of the fusion coefficient. Update the velocity and position of each particle, handle out-of-bounds positions and calculate the fitness of the new position, and update the individual and global historical bests; Calculate the distance between particles. If the distance between two particles is less than the repulsion radius, compare their fitness. Randomly reset the particles with poor fitness and recalculate their fitness. Record the current global optimum value, check the current iteration count. If the maximum iteration count has not been reached, return to the update step; otherwise, output the global optimum fusion coefficient.

[0025] In one aspect of this disclosure, a grid-connection control stability optimization device adapted to grid strength characteristics is provided, comprising: The system modeling module is used to establish a small-signal state-space model of the inverter grid-connection integrated control system. The inverter grid-connection integrated control system includes a grid-connection integrated controller, an inverter, a load, a transformer, lines, and a power grid. The grid-connection integrated controller weights and fuses the angular frequency of the grid-connection control output with the angular frequency of the grid-connection control output using a fusion coefficient to obtain the system angular frequency. Based on the system angular frequency, it generates a front-end voltage reference value, which is then output to the inverter after dual closed-loop control. The model analysis module is used to analyze the stability of the inverter grid-connection control system under different grid strengths and different fusion coefficients based on the small-signal state-space model. The parameter optimization module is used to solve for the optimal fusion coefficient with the stability of the inverter grid-connection and grid-integration control system as the objective and the stability of the inverter grid-connection and grid-integration control system as the constraint. The optimal fusion coefficient is then applied to the grid-connection and grid-integration controller to achieve inverter grid-connection and grid-integration control. The optimal fusion coefficient is the fusion coefficient that minimizes the real part of the dominant eigenvalue of the inverter grid-connection and grid-integration control system.

[0026] An exemplary embodiment of this disclosure discloses a grid-connection control stability optimization method adapted to grid strength characteristics, comprising: establishing a small-signal state-space model of the inverter grid-connection control system; analyzing the system stability under different grid strengths and different fusion coefficients; and, with optimal system stability as the objective and system stability as the constraint, solving for the optimal fusion coefficient that minimizes the real part of the dominant eigenvalue of the system and applying it to the controller. The grid-connection controller obtains the system angular frequency by weighted fusion of the grid-connection angular frequency controlled by the virtual synchronous machine and the grid-connection angular frequency controlled by the phase-locked loop using the fusion coefficient. This invention adapts to changes in grid strength without switching control modes, avoiding transient inrush currents; it only requires adjusting the fusion coefficient without modifying the original controller parameters; and it uses a niche-based particle swarm optimization method to solve for the optimal fusion coefficient, ensuring global optimality and significantly improving system stability margin.

[0027] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0028] The above and other features and advantages of this disclosure will become more apparent from the detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0029] Figure 1 A flowchart is shown below for a grid-connection control stability optimization method adapted to grid strength characteristics, according to an exemplary embodiment of the present disclosure. Figure 2 The topology of an inverter grid-connection integrated control system for a grid-connection integrated control stability optimization method adapted to grid strength characteristics, according to an exemplary embodiment of the present disclosure, is shown. Figure 3 This diagram illustrates the trend of the dominant characteristic value of the system under strong grid conditions, according to an exemplary embodiment of the present disclosure, of a grid-connection control stability optimization method adapted to the grid strength characteristics. Figure 4 This diagram illustrates the trend of the dominant characteristic value of a system under weak grid conditions, according to an exemplary embodiment of the present disclosure, which is a grid-connection control stability optimization method adapted to the grid strength characteristics. Figure 5 A flowchart of a particle swarm optimization algorithm for solving the grid-connection control stability optimization method adapted to the grid strength characteristics is shown in an exemplary embodiment of the present disclosure. Figure 6 A schematic block diagram of a grid-connection control stability optimization device adapted to grid strength characteristics is shown according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0030] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0031] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, materials, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, materials, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0032] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0033] In this example embodiment, a grid-connection control stability optimization method adapted to the grid strength characteristics is first provided; refer to Figure 1 As shown, the grid-connection control stability optimization method adapted to the grid strength characteristics may include the following steps: Step S110: Establish a small-signal state-space model of the inverter grid-connection and grid-integration control system. The inverter grid-connection and grid-integration control system includes a grid-connection and grid-integration controller, an inverter, a load, a transformer, lines, and a power grid. The grid-connection and grid-integration controller weights and fuses the angular frequency of the grid-connection control output with the angular frequency of the grid-connection control output using a fusion coefficient to obtain the system angular frequency. Based on the system angular frequency, it generates a front-end voltage reference value, which is then output to the inverter after dual closed-loop control. Step S120: Based on the small-signal state-space model, analyze the stability of the inverter grid-connection control system under different grid strengths and different fusion coefficients. Step S130: With the goal of optimizing the stability of the inverter grid-connection and grid-integration control system and with the stability of the inverter grid-connection and grid-integration control system as a constraint, solve for the optimal fusion coefficient, and apply the optimal fusion coefficient to the grid-connection and grid-integration controller to realize the inverter grid-connection and grid-integration control. The optimal fusion coefficient is the fusion coefficient that minimizes the real part of the dominant eigenvalue of the inverter grid-connection and grid-integration control system.

[0034] An exemplary embodiment of this disclosure discloses a grid-connection control stability optimization method adapted to grid strength characteristics, comprising: establishing a small-signal state-space model of the inverter grid-connection control system; analyzing the system stability under different grid strengths and different fusion coefficients; and, with optimal system stability as the objective and system stability as the constraint, solving for the optimal fusion coefficient that minimizes the real part of the dominant eigenvalue of the system and applying it to the controller. The grid-connection controller obtains the system angular frequency by weighted fusion of the grid-connection angular frequency controlled by the virtual synchronous machine and the grid-connection angular frequency controlled by the phase-locked loop using the fusion coefficient. This invention adapts to changes in grid strength without switching control modes, avoiding transient inrush currents; it only requires adjusting the fusion coefficient without modifying the original controller parameters; and it uses a niche-based particle swarm optimization method to solve for the optimal fusion coefficient, ensuring global optimality and significantly improving system stability margin.

[0035] The following will further explain a grid-connection control stability optimization method adapted to the grid strength characteristics in this example embodiment.

[0036] Example 1: In step S110, a small-signal state-space model of the inverter grid-connection control system can be established. The inverter grid-connection control system includes a grid-connection controller, an inverter, a load, a transformer, a line, and a power grid. The grid-connection controller weights and fuses the angular frequency of the grid-connection control output with the angular frequency of the grid-connection control output using a fusion coefficient to obtain the system angular frequency. Based on the system angular frequency, a front-end voltage reference value is generated and output to the inverter after dual closed-loop control.

[0037] In this example embodiment, the method further includes: The network-type control uses virtual synchronous machine control, and the follow-network type control uses phase-locked loop control. The mesh-type control outputs the mesh-type angular frequency, and the following mesh-type control outputs the following mesh angular frequency; The system angular frequency is obtained by weighted fusion of the meshing angular frequency and the tracking angular frequency using the fusion coefficient, and the fusion formula is: , in,

[0038] In the formula: V o,q , I L , I lo , I c , ω f , I o , K pll p , K pll i These are the inverter outputs. q The integral of the shaft voltage, the filter inductor current, the load current, the line current, the cutoff angular frequency, the disturbance current, the phase-locked loop proportional coefficient, and the phase-locked loop integral coefficient.

[0039] In this example embodiment, the grid-connection controller further includes a voltage loop, a current loop, and a reactive power control loop. The small-signal model of the voltage loop is: , in,

[0040] In the formula: , , K up , K ui These are the integral of the voltage loop controller output, the setpoint of the filter inductor current, the voltage loop proportional coefficient, and the voltage loop integral coefficient, respectively. The small-signal model of the current loop is: , in,

[0041] In the formula: , , K ip , K ii These are the integral of the current loop controller output, the modulation voltage, the current loop proportional coefficient, and the current loop integral coefficient, respectively. The reactive power control loop adopts reactive power droop control, and generates a voltage amplitude reference value based on the rated reactive power, actual reactive power and rated voltage.

[0042] In step S120, the stability of the inverter grid-connection control system under different grid strengths and different fusion coefficients can be analyzed based on the small-signal state-space model.

[0043] In this example embodiment, the establishment of the small-signal state-space model in the method includes: Small-signal models for the grid-connected controller, voltage loop, current loop, inverter, load, and line are established separately. The inverter small-signal model is as follows: , in, ; The load small-signal model is as follows: , in,

[0044] In the formula: r load , L load These are the load resistance and the load inductance, respectively. The line small-signal model is as follows: , in, ; By combining the small-signal models, we obtain the small-signal state-space model of the overall system:

[0045] in,

[0046] In the formula: x s , u s These are the state variables and input variables in the small-signal model of the system, respectively. A s , B s These are the state matrix and input matrix in the small-signal model of the system, respectively.

[0047] In this example embodiment, the method further includes: The overall system small-signal state-space model has 16 state variables and the state matrix is ​​a 16×16 matrix. After solving for the eigenvalues ​​of the state matrix, a zero eigenvalue was found. Participation factor analysis was used to identify the relationship between this zero eigenvalue and the state variable Δ. λ d Δ λ q Related; analysis shows that the state variable Δ λ d Δ λ q To eliminate redundant state variables, the small-signal state-space model of the overall system is reduced in order to obtain a reduced-order small-signal model of the system.

[0048] in, .

[0049] In step S130, the optimal fusion coefficient can be solved with the goal of optimizing the stability of the inverter grid-connection and grid-integration control system and the stability of the inverter grid-connection and grid-integration control system as a constraint. The optimal fusion coefficient is then applied to the grid-connection and grid-integration controller to achieve inverter grid-connection and grid-integration control. The optimal fusion coefficient is the fusion coefficient that minimizes the real part of the dominant eigenvalue of the inverter grid-connection and grid-integration control system.

[0050] In this example embodiment, the grid strength in the method is characterized by the short-circuit ratio, which is calculated using the following formula:

[0051] In the formula: S SC , S n , U n , Z line These are the short-circuit capacity of the inverter's grid connection node, the inverter's rated apparent power, the rated grid voltage, and the line impedance between the inverter and the grid.

[0052] In this example embodiment, the stability analysis of the method includes: under a fixed power grid strength, changing the value of the fusion coefficient, solving the dominant eigenvalue of the small-signal state-space model, and obtaining the stability trend of the dominant eigenvalue as the fusion coefficient changes; Under strong power grid conditions, as the fusion coefficient gradually increases from 0 to 1, the trend of the real part of the dominant eigenvalue is: left shift, right shift, left shift, right shift, left shift, right shift, unchanged; Under weak grid conditions, as the fusion coefficient gradually increases from 0 to 1, the trend of the real part of the dominant eigenvalue is: left shift, right shift, or no change.

[0053] In this example embodiment, the method further includes: The solution for the optimal fusion coefficient is to minimize the real part of the largest eigenvalue, with the constraint that the real parts of all eigenvalues ​​of the system are less than 0. The objective function is: : The constraint condition is: : in, A sj Let be the state matrix of the small-signal state-space model of the reduced-order system. The eig() function is used to find the eigenvalues, and the real() function is used to find the real part.

[0054] In this example embodiment, the optimal fusion coefficient is solved using a niche-based particle swarm optimization method, which includes: Establish a system state-space model and obtain the steady-state operating points of voltage and current; Input the fusion coefficient, construct the system small signal model and calculate the feature value. Take minimizing the maximum real part of the dominant feature value as the objective function, set the niche particle swarm parameters, and initialize the particle swarm position and velocity. The position represents the value of the fusion coefficient. Update the velocity and position of each particle, handle out-of-bounds positions and calculate the fitness of the new position, and update the individual and global historical bests; Calculate the distance between particles. If the distance between two particles is less than the repulsion radius, compare their fitness. Randomly reset the particles with poor fitness and recalculate their fitness. Record the current global optimum value, check the current iteration count. If the maximum iteration count has not been reached, return to the update step; otherwise, output the global optimum fusion coefficient.

[0055] Example 2: This invention addresses the issue of decreased system stability or even instability caused by distributed energy access to the grid via grid-connected or grid-connected control. It proposes a grid-connected-grid fusion control method for inverters that considers grid strength. Based on the grid-connected virtual synchronous machine control structure, this method introduces grid-connected phase-locked loop (PLL) technology and performs weighted fusion, aiming to maximize the system's stability margin while ensuring system stability under different grid strength conditions. Compared to other methods, this control strategy has the following advantages: (1) The proposed grid-connected-grid fusion control method does not require switching the inverter control mode to adapt to changes in grid strength, avoiding the generation of transient inrush currents and their impact on system transient stability. (2) This method can optimize the optimal fusion coefficient based on the strength of the inverter's grid connection node, avoiding the long debugging cycle and difficulty in guaranteeing the optimal result of traditional trial-and-error or empirical formula methods, resulting in high design efficiency. (3) Under different grid strength conditions, the proposed method only needs to adjust the grid-connected-grid fusion coefficient, without adjusting other controller parameters. Furthermore, the established small-signal model does not ignore coupling or excessively reduce the order, resulting in more accurate optimization results.

[0056] Step S110: Small-signal state-space modeling of the inverter grid-connected control system. The topology of the inverter grid-connection control system is shown in the figure below. The system includes the inverter, load, transformer, line, and power grid for grid-connection control.

[0057] Figure 2 middle, ω 0、 ω GFM , K p , P ref , P , J , D , U 0、 K q , Q ref , Q , ω GFL , k , U i , U o , L f , C f , r f , U refThese are: rated angular frequency, grid output angular frequency, active power droop coefficient, rated active power, actual active power, moment of inertia, damping coefficient, rated voltage, reactive power droop coefficient, rated reactive power, actual reactive power, grid output angular frequency, grid-grid weighted fusion coefficient, input voltage, output voltage, filter inductance, filter capacitance, equivalent internal resistance, and front-end voltage of the grid-connected control inverter. U pcc , L line , r line , U g , ω These are the grid connection point voltage, line inductance, line resistance, grid voltage, and system angular frequency.

[0058] In this example embodiment, the small-signal model of the network-follower fusion controller includes: Figure 2 If the grid connection point voltage is equal to the inverter output voltage, then the small-signal model of the inverter's grid-connection integrated controller is: (1) in

[0059] In the formula: V o,q , I L , I lo , I c , ω f , I o , K pll p , K pll i These are the inverter outputs. q The integral of the shaft voltage, the filter inductor current, the load current, the line current, the cutoff angular frequency, the disturbance current, the phase-locked loop proportional coefficient, and the phase-locked loop integral coefficient.

[0060] The voltage loop small-signal model is as follows: (2) in

[0061] In the formula: , , K up, K ui These are the integral of the voltage loop controller output, the setpoint of the filter inductor current, the voltage loop proportional coefficient, and the voltage loop integral coefficient, respectively.

[0062] The small-signal model of the current loop is: (3) in

[0063] In the formula: , , K ip , K ii These are the integral of the current loop controller output, the modulation voltage, the current loop proportional coefficient, and the current loop integral coefficient, respectively.

[0064] In this example embodiment, the inverter small-signal model includes: The small-signal model of the inverter is: (4) in

[0065] In this example embodiment, the load small-signal model includes: The load small-signal model is as follows: (5) in

[0066] In the formula: r load , L load These are the load resistance and load inductance, respectively.

[0067] In this example embodiment, the line small-signal model includes: The line small-signal model is as follows: (6) in

[0068] In this example embodiment, the overall system small-signal model includes: Combining equations (1) and (5), the overall small-signal model of the inverter grid-connection integrated control system can be obtained as follows: (7) in

[0069] In the formula: x s , u s These are the state variables and input variables in the small-signal model of the system, respectively. A s , B s These are the state matrix and input matrix in the small-signal model of the system, respectively.

[0070] As shown in equation (7), the overall small-signal model of the inverter grid-connected control system has 16 state variables and a 16×16 matrix. However, after solving for the eigenvalues ​​of this state matrix, it was found that there are 0 eigenvalues, indicating that the system is in a critical stable state, which is inconsistent with the actual situation. Through participation factor analysis, it can be seen that the 0 eigenvalues ​​are mainly related to the state variable Δ λ d Δ λ q Related. Upon inspection and analysis, it was found that the integral coefficient of the current loop is 0, therefore the state variable Δ... λ d Δ λ q In fact, it does not participate in any dynamic process and has no effect on the output, therefore Δ λ d Δ λ q Redundant state variables can be eliminated by reducing the order of the system model. The reduced-order small-signal model of the system is as follows: (8) in

[0071] Solve the state matrix in equation (8) A sj The discovery that there are no zero eigenvalues ​​proves that redundant variables were eliminated after the system was reduced in order, and the eigenvalues ​​of the constructed system model can accurately reflect the stability of the system.

[0072] Step S120: System stability analysis under different power grid strengths and integration coefficients The impact of grid strength and the grid-connection weighted fusion coefficient on system stability is complex; higher grid strength and a larger grid-connection weighted fusion coefficient do not necessarily guarantee better system stability. Under strong grid conditions, the virtual synchronous machine control in the grid may generate coupled oscillations with line impedance, reducing system stability. Grid strength is represented by the short-circuit ratio (SCR). (9) In the formula: SSC , S n , U n , Z line These are the short-circuit capacity of the inverter's grid connection node, the inverter's rated apparent power, the rated grid voltage, and the line impedance between the inverter and the grid.

[0073] As shown in equation (9), the short-circuit ratio is determined by the grid rated voltage, line parameters, and inverter rated power. Assuming the grid rated voltage and inverter rated power remain constant, changing the line parameters will change the short-circuit ratio, and thus the grid strength. The dominant characteristic values ​​of the system are solved under different grid strengths and grid-connection weighted fusion coefficients, and the changing trends of the dominant characteristic values ​​of the system with the increase of the grid-connection weighted fusion coefficient under different grid strengths are plotted, such as... Figures 3-4 As shown.

[0074] Depend on Figure 3 , Figure 4 It can be seen that as the weighted fusion coefficient between the grid construction and grid integration gradually increases from 0 to 1, under strong grid conditions, the trend of the real part of the dominant eigenvalue of the system is: left-right-left-right-left-right-unchanged; under weak grid conditions, the trend is: left-right-unchanged. That is, the system stability may increase, decrease, or remain unchanged. Therefore, the relationship between system stability and the grid construction-grid integration coefficient is non-linear under different grid intensities, making it difficult to directly solve for the optimal integration coefficient based on grid intensity to achieve the strongest system stability.

[0075] Step S130: Solve the stability improvement method of inverter network construction-network fusion control based on the improved particle swarm optimization. To address the aforementioned challenges, a solution method based on improved particle swarm optimization for inverter grid-connection fusion control stability improvement is proposed, optimizing the optimal fusion coefficients for nodes with varying grid strengths. The objective function is to minimize the real part of the largest eigenvalue, i.e., to achieve the highest system stability. (10) The constraint condition is that the real part of all eigenvalues ​​of the system is less than 0, i.e., the system is stable. (11) However, the state matrix in equations (10) and (11) A sj There exists U o,dq , I L,dq With equal voltage and current variables, and with the fusion coefficient k The value changes with the fusion coefficient and cannot be calculated as a fixed value; simulation data needs to be collected. Verification has shown that the fusion coefficient...k Changes, disturbance current I o,dq The change is small and has little impact on system stability; therefore, it can be treated as a fixed value, allowing for the calculation of the required parameters without repeatedly running the simulation model and collecting data. The state matrix can then be used as the basis for this calculation. A sj The required variables are the output system state-space model, which is: (12) in

[0076] A niche-based particle swarm optimization method is used to solve the problem, avoiding local extrema during the search process, ensuring global optimization, and significantly improving the algorithm's execution efficiency. The specific steps are as follows: ① Establish a system state-space model and obtain the steady-state values ​​of voltage and current; ② Input the fusion coefficients. k 1. Construct a small-signal model of the system and calculate eigenvalues, using minimizing the maximum real part of the dominant eigenvalue as the objective function; 2. Set the niche particle swarm parameters and initialize the particle swarm position and velocity; 3. Update the particle swarm position and velocity, calculate the distance between particles, and if the distance between a pair of particles is less than the repulsion radius, compare fitness and randomly reset the poorer particles; 4. Check the current iteration count. If the maximum iteration count has not been reached, return to step 3; otherwise, proceed to the next step; 5. Output the globally optimal fusion coefficient. k Algorithm flow for particle swarm optimization solution based on niche-based mesh construction-follower mesh fusion coefficients. Figure 5 As shown.

[0077] In this example embodiment, addressing the system stability degradation or even instability caused by distributed energy access to the grid via grid-connected or grid-integrated control, this paper proposes a stability improvement method for inverter grid-connected control that considers grid strength. First, based on the topology of the inverter grid-connected control system, a small-signal model is established, including models for the grid-connected controller, voltage and current loops, the inverter, line loads, and the overall system. Second, the grid strength is represented by the short-circuit ratio at the grid connection point, calculated based on line, grid, and inverter parameters. Simulation methods are used to analyze system stability under different grid strengths and integration coefficients. Finally, a system state-space model is established to obtain the steady-state operating point. The objective function is minimizing the real part of the maximum eigenvalue, with the constraint that the real parts of all system eigenvalues ​​are less than 0. A niche-based particle swarm optimization method is used to solve for the optimal grid-connected integration coefficient, minimizing the dominant eigenvalue when distributed energy accesses nodes with different grid strengths, thereby improving system stability.

[0078] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0079] Furthermore, in this example embodiment, a grid-connection control stability optimization device adapted to the grid strength characteristics is also provided. (Refer to...) Figure 6 As shown, the grid-connection control stability optimization device 200 adapted to the grid strength characteristics may include: a system modeling module 210, a model analysis module 220, and a parameter optimization module 230. Wherein: The system modeling module 210 is used to establish a small-signal state-space model of the inverter grid-connection integrated control system. The inverter grid-connection integrated control system includes a grid-connection integrated controller, an inverter, a load, a transformer, lines, and a power grid. The grid-connection integrated controller weights and fuses the angular frequency of the grid-connection control output with the angular frequency of the grid-connection control output using a fusion coefficient to obtain the system angular frequency. Based on the system angular frequency, it generates a front-end voltage reference value, which is then output to the inverter after dual closed-loop control. The model analysis module 220 is used to analyze the stability of the inverter grid-connection control system under different grid strengths and different fusion coefficients based on the small-signal state-space model. The parameter optimization module 230 is used to solve for the optimal fusion coefficient with the stability of the inverter grid-connection and grid-integration control system as the objective and the stability of the inverter grid-connection and grid-integration control system as the constraint condition, and apply the optimal fusion coefficient to the grid-connection and grid-integration controller to realize the inverter grid-connection and grid-integration control. The optimal fusion coefficient is the fusion coefficient that minimizes the real part of the dominant eigenvalue of the inverter grid-connection and grid-integration control system.

[0080] The specific details of each of the above-mentioned grid-connection and grid-integrated control stability optimization device modules adapted to the grid strength characteristics have been described in detail in the corresponding grid-connection and grid-integrated control stability optimization method adapted to the grid strength characteristics, so they will not be repeated here.

[0081] It should be noted that although several modules or units of a grid-connection and grid-integrated control stability optimization device 200 adapted to the grid strength characteristics are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0082] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0083] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0084] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A grid-connection control stability optimization method adapted to the grid strength characteristics, characterized in that, The method includes: A small-signal state-space model of the inverter grid-connection integrated control system is established. The inverter grid-connection integrated control system includes a grid-connection integrated controller, an inverter, a load, a transformer, lines, and a power grid. The grid-connection integrated controller weights and fuses the angular frequency of the grid-connection control output with the angular frequency of the grid-connection control output using a fusion coefficient to obtain the system angular frequency. Based on the system angular frequency, a front-end voltage reference value is generated and output to the inverter after dual closed-loop control. Based on the small-signal state-space model, the stability of the inverter grid-connection control system under different grid strengths and different fusion coefficients is analyzed. With the goal of optimizing the stability of the inverter grid-connection and grid-integration control system and the constraint of the stability of the inverter grid-connection and grid-integration control system, the optimal fusion coefficient is solved, and the optimal fusion coefficient is applied to the grid-connection and grid-integration controller to realize the inverter grid-connection and grid-integration control. The optimal fusion coefficient is the fusion coefficient that minimizes the real part of the dominant eigenvalue of the inverter grid-connection and grid-integration control system.

2. The method as described in claim 1, characterized in that, The method further includes: The network-type control uses virtual synchronous machine control, and the follow-network type control uses phase-locked loop control. The mesh-type control outputs the mesh-type angular frequency, and the following mesh-type control outputs the following mesh angular frequency; The system angular frequency is obtained by weighted fusion of the meshing angular frequency and the tracking angular frequency using the fusion coefficient, and the fusion formula is: ， in, In the formula: V o,q , I L , I lo , I c , ω f , I o , K pll p , K pll i These are the inverter outputs. q The integral of the shaft voltage, the filter inductor current, the load current, the line current, the cutoff angular frequency, the disturbance current, the phase-locked loop proportional coefficient, and the phase-locked loop integral coefficient.

3. The method as described in claim 2, characterized in that, In the method, the grid-connection controller further includes a voltage loop, a current loop, and a reactive power control loop; The small-signal model of the voltage loop is: ， in, In the formula: , , K up , K ui These are the integral of the voltage loop controller output, the setpoint of the filter inductor current, the voltage loop proportional coefficient, and the voltage loop integral coefficient, respectively. The small-signal model of the current loop is: ， in, In the formula: , , K ip , K ii These are the integral of the current loop controller output, the modulation voltage, the current loop proportional coefficient, and the current loop integral coefficient, respectively. The reactive power control loop adopts reactive power droop control, and generates a voltage amplitude reference value based on the rated reactive power, actual reactive power and rated voltage.

4. The method as described in claim 3, characterized in that, The establishment of the small-signal state-space model in the method includes: Small-signal models for the grid-connected controller, voltage loop, current loop, inverter, load, and line are established separately. The inverter small-signal model is as follows: ， in, ； The load small-signal model is as follows: ， in, In the formula: r load , L load These are the load resistance and load inductance, respectively. The line small-signal model is as follows: ， in, ; By combining the small-signal models, we obtain the small-signal state-space model of the overall system: in, In the formula: x s , u s These are the state variables and input variables in the small-signal model of the system, respectively. A s , B s These are the state matrix and input matrix in the small-signal model of the system, respectively.

5. The method as described in claim 4, characterized in that, The method further includes: The overall system small-signal state-space model has 16 state variables and the state matrix is ​​a 16×16 matrix. After solving for the eigenvalues ​​of the state matrix, a zero eigenvalue was found. Participation factor analysis was used to identify the relationship between this zero eigenvalue and the state variable Δ. λ d Δ λ q Related; analysis shows that the state variable Δ λ d Δ λ q To eliminate redundant state variables, the small-signal state-space model of the overall system is reduced in order to obtain a reduced-order small-signal model of the system. in, 。 6. The method as described in claim 5, characterized in that, In the method, the power grid strength is characterized by the short-circuit ratio, and the formula for calculating the short-circuit ratio is: In the formula: S SC , S n , U n , Z line These are the short-circuit capacity of the inverter's grid connection node, the inverter's rated apparent power, the rated grid voltage, and the line impedance between the inverter and the grid.

7. The method as described in claim 6, characterized in that, The stability analysis of the method includes: under a fixed power grid strength, changing the value of the fusion coefficient, solving the dominant eigenvalue of the small-signal state-space model, and obtaining the stability trend of the dominant eigenvalue as the fusion coefficient changes; Under strong power grid conditions, as the fusion coefficient gradually increases from 0 to 1, the trend of the real part of the dominant eigenvalue is: left shift, right shift, left shift, right shift, left shift, right shift, unchanged; Under weak grid conditions, as the fusion coefficient gradually increases from 0 to 1, the trend of the real part of the dominant eigenvalue is: left shift, right shift, or no change.

8. The method as described in claim 7, characterized in that, The method further includes: The solution for the optimal fusion coefficient is to minimize the real part of the largest eigenvalue, with the constraint that the real parts of all eigenvalues ​​of the system are less than 0. The objective function is: : The constraint condition is: : in, A sj Let be the state matrix of the small-signal state-space model of the reduced-order system. The eig() function is used to find the eigenvalues, and the real() function is used to find the real part.

9. The method as described in claim 8, characterized in that, In the method described above, the optimal fusion coefficient is solved using a niche-based particle swarm optimization method, which includes: Establish a system state-space model and obtain the steady-state operating points of voltage and current; Input the fusion coefficient, construct the system small signal model and calculate the feature value. Take minimizing the maximum real part of the dominant feature value as the objective function, set the niche particle swarm parameters, and initialize the particle swarm position and velocity. The position represents the value of the fusion coefficient. Update the velocity and position of each particle, handle out-of-bounds positions and calculate the fitness of the new position, and update the individual and global historical bests; Calculate the distance between particles. If the distance between two particles is less than the repulsion radius, compare their fitness. Randomly reset the particles with poor fitness and recalculate their fitness. Record the current global optimum value, check the current iteration count. If the maximum iteration count has not been reached, return to the update step; otherwise, output the global optimum fusion coefficient.

10. A grid-connection control stability optimization device adapted to the grid strength characteristics, characterized in that, The device includes: The system modeling module is used to establish a small-signal state-space model of the inverter grid-connection integrated control system. The inverter grid-connection integrated control system includes a grid-connection integrated controller, an inverter, a load, a transformer, lines, and a power grid. The grid-connection integrated controller weights and fuses the angular frequency of the grid-connection control output with the angular frequency of the grid-connection control output using a fusion coefficient to obtain the system angular frequency. Based on the system angular frequency, it generates a front-end voltage reference value, which is then output to the inverter after dual closed-loop control. The model analysis module is used to analyze the stability of the inverter grid-connection control system under different grid strengths and different fusion coefficients based on the small-signal state-space model. The parameter optimization module is used to solve for the optimal fusion coefficient with the stability of the inverter grid-connection and grid-integration control system as the objective and the stability of the inverter grid-connection and grid-integration control system as the constraint. The optimal fusion coefficient is then applied to the grid-connection and grid-integration controller to achieve inverter grid-connection and grid-integration control. The optimal fusion coefficient is the fusion coefficient that minimizes the real part of the dominant eigenvalue of the inverter grid-connection and grid-integration control system.

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