A method and system for optimizing grid connection parameters of a distributed photovoltaic system

Abstract

This invention discloses a method and system for optimizing grid-connected parameters of a distributed photovoltaic (PV) system. The method includes: first, establishing a small-signal mathematical model of the distributed PV power generation system, comprising PV cells, a grid-connected inverter, a transformer, and the power grid; second, using eigenvalue analysis to obtain the boundary values ​​of the system parameters under stable conditions for each eigenvalue; third, establishing optimization objective functions for small-disturbance stability, damping ratio, and stability margin; and finally, using an improved exponential trigonometric optimization algorithm to optimize the objective functions, thereby determining the optimal grid-connected parameters of the distributed PV system. This significantly improves the grid-connected stability of the distributed PV system, thus ensuring its stable operation.

Description

Technical Field

[0001] This invention belongs to the field of grid connection parameter optimization technology, and in particular relates to a method and system for optimizing grid connection parameters of a distributed photovoltaic system. Background Technology

[0002] Currently, distributed photovoltaic (PV) systems have surpassed centralized PV systems. However, with the surge in the number of distributed PV systems, the transient voltage stability level is difficult to improve effectively when they are connected to the grid. Grid connection parameters, as the key to grid connection of distributed PV power sources, play an important role in distributed PV power generation systems and need to be able to adapt to different operating conditions and system dynamic characteristics in order to achieve better performance and power quality.

[0003] Most research, both domestic and international, focuses on modeling distributed photovoltaic (PV) power generation systems connected to the grid and studying control methods for interface inverters. Research on stability analysis from a control perspective is relatively limited. Furthermore, these studies do not consider the coupling relationships between various controller parameters in detail; they only analyze the impact of parameter changes on system stability, failing to achieve a relatively globally optimal system parameter level. Summary of the Invention

[0004] This invention provides a method and system for optimizing grid-connected parameters of a distributed photovoltaic system, which addresses the technical problem of failing to achieve relative global optimality of system parameters.

[0005] In a first aspect, the present invention provides a method for optimizing grid connection parameters of a distributed photovoltaic system, comprising:

[0006] Constructing a small-signal mathematical model for a distributed photovoltaic power generation system;

[0007] Based on the small-signal mathematical model, the boundary values ​​of the system parameters under stable conditions for each eigenvalue are obtained by eigenvalue analysis.

[0008] Based on the boundary values ​​of the system parameters, establish a coordinated optimization objective function for small disturbance stability, damping ratio, and stability margin;

[0009] The improved exponential trigonometric optimization algorithm is used to optimize the objective function, thereby obtaining the optimal grid connection parameters for the distributed photovoltaic system.

[0010] Secondly, the present invention provides a grid connection parameter optimization system for a distributed photovoltaic system, comprising:

[0011] The module is configured to build a small-signal mathematical model for a distributed photovoltaic power generation system.

[0012] The determination module is configured to obtain the boundary values ​​of system parameters under stable conditions for each eigenvalue based on the small-signal mathematical model using the eigenvalue analysis method.

[0013] A module is established and configured to create a coordinated optimization objective function for small-disturbance stability, damping ratio, and stability margin based on the boundary values ​​of the system parameters.

[0014] The optimization module is configured to use an improved exponential triangular optimization algorithm to optimize the objective function and obtain the optimal grid connection parameters for the distributed photovoltaic system.

[0015] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the grid connection parameter optimization method for a distributed photovoltaic system according to any embodiment of the present invention.

[0016] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the grid-connected parameter optimization method for a distributed photovoltaic system according to any embodiment of the present invention.

[0017] The grid connection parameter optimization method and system of the distributed photovoltaic system disclosed in this application adopts an improved exponential triangular optimization algorithm to optimize the grid connection parameters of the distributed photovoltaic system, and the obtained grid connection parameters effectively improve the voltage stability level. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for optimizing grid-connected parameters of a distributed photovoltaic system according to an embodiment of the present invention;

[0020] Figure 2 A flowchart of an improved exponential trigonometric optimization algorithm provided in an embodiment of the present invention;

[0021] Figure 3 A comparison diagram of an improved exponential triangular optimization algorithm provided in an embodiment of the present invention;

[0022] Figure 4 This is a structural block diagram of a grid connection parameter optimization system for a distributed photovoltaic system provided in an embodiment of the present invention;

[0023] Figure 5This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0024] 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. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Please see Figure 1 The flowchart illustrates a method for optimizing grid-connected parameters of a distributed photovoltaic system according to this application.

[0026] like Figure 1 As shown, the method for optimizing grid connection parameters of a distributed photovoltaic system specifically includes the following steps:

[0027] Step S101: Construct a small-signal mathematical model for the distributed photovoltaic power generation system.

[0028] In this step, the expression for the small-signal mathematical model is:

[0029] ,

[0030] ,

[0031] ,

[0032] ,

[0033] ,

[0034] ,

[0035] ,

[0036] ,

[0037] In the formula, The output current of the photovoltaic cell. This refers to the short-circuit current of the photovoltaic cell. , All are constant coefficients. The output voltage of the photovoltaic cell. This is the open-circuit voltage of the photovoltaic cell. For battery temperature, For ambient temperature, For temperature coefficient, Light intensity, This is the short-circuit reference current for the photovoltaic cell. , , All are compensation coefficients. The temperature of the photovoltaic cell under non-standard conditions. For reference temperature, The short-circuit reference irradiance for photovoltaic cells. This is the short-circuit reference voltage for the photovoltaic cell. , All are linear fitting parameters. This is the current when the photovoltaic cell operates at its maximum power point. This is the reference current when the photovoltaic cell operates at its maximum power point. This is the voltage at which the photovoltaic cell operates at its maximum power point. This is the reference voltage for when the photovoltaic cell operates at its maximum power point. The base is the natural number;

[0038] ,

[0039] ,

[0040] ,

[0041] In the formula, DC voltage It is a DC bus energy storage capacitor. This represents the d-axis voltage of the photovoltaic grid connection. For grid-connected photovoltaic systems, the d-axis current is... The equivalent reactance of the transformer and transmission line. For voltage angle, This is the equivalent resistance of the transformer and transmission line. For grid-connected photovoltaic systems, This refers to the transformer turns ratio.

[0042] Step S102: Based on the small-signal mathematical model, the boundary values ​​of the system parameters under stable conditions for each eigenvalue are obtained using the eigenvalue analysis method.

[0043] In this step, based on the small-signal mathematical model, the eigenvalue analysis method is used to draw the eigenvalue root locus diagrams corresponding to different system parameters for analysis, and to obtain the system parameter boundary values ​​under the stable condition of each eigenvalue.

[0044] Step S103: Establish a coordinated optimization objective function for small disturbance stability, damping ratio, and stability margin based on the system parameter boundary values.

[0045] In this step, the comprehensive optimization objective function, which includes the volatility, variability, and multiple operating scenarios of the microgrid operation, is processed to obtain the coordinated optimization objective function. The expression of the coordinated optimization objective function is as follows:

[0046] ,

[0047] In the formula, To coordinate and optimize the objective function, Let be the objective function formed by the eigenvalues ​​with positive real parts in the distributed photovoltaic system. For distributed photovoltaic systems, the eigenvalue damping ratio is less than a given damping ratio. The objective function is composed of the eigenvalues ​​of . For a distributed photovoltaic system, the real part of the eigenvalues ​​is less than 0 and greater than a given real part. The objective function is composed of the eigenvalues.

[0048] The objective function for calculating the eigenvalues ​​with positive real parts in a distributed photovoltaic system is expressed as follows:

[0049] ,

[0050] ,

[0051] In the formula, Let be the real part of the i-th eigenvalue in the distributed photovoltaic system whose real part is greater than 0. The weighting coefficient for the i-th eigenvalue with a real part greater than 0 in a distributed photovoltaic system. Empirical parameters that are greater than 0;

[0052] The eigenvalue damping ratio in a distributed photovoltaic system is less than a given damping ratio. The expression for the objective function formed by the eigenvalues ​​is:

[0053] ,

[0054] ,

[0055] In the formula, For the j-th less than eigenvalue damping ratio, For the j-th less than The weighting coefficient of the eigenvalue damping ratio, The imaginary part of the j-th eigenvalue;

[0056] In calculating the eigenvalues ​​of a distributed photovoltaic system, the real part is less than 0 and greater than a given real part. The expression for the objective function formed by the eigenvalues ​​is:

[0057] ,

[0058] ,

[0059] In the formula, The real part of the k-th eigenvalue is less than 0 and greater than 0. The weighting coefficients of the real part of the eigenvalues, The real part of the k-th eigenvalue is less than 0 and greater than 0. The real part of the eigenvalues.

[0060] Step S104: The improved exponential trigonometric optimization algorithm is used to optimize the objective function to obtain the optimal grid connection parameters of the distributed photovoltaic system.

[0061] In this step, refer to Figure 2 The improved exponential triangular optimization algorithm executes the following steps: First, it sets the initial population and the maximum number of iterations. Then, it uses a constrained exploration method to determine the upper and lower bounds. Next, it calculates the fitness. Then, it updates the fitness based on the exploration mode and development model. Finally, it determines whether the maximum number of iterations has been reached. Specifically:

[0062] A constraint search strategy is used to update the iteration count, and after updating the iteration count, the upper and lower limits of the update search space are determined. The expression for the constraint search strategy is:

[0063] ,

[0064] ,

[0065] In the formula, For the updated iteration count, For the iteration count before the update, This is a function in MATLAB that performs rounding operations. This represents the current iteration number. The maximum number of iterations, , All are adjustment coefficients;

[0066] The expressions for determining the upper and lower bounds of the update search space are:

[0067] ,

[0068] ,

[0069] In the formula, , These are the upper and lower limits of the expected search space, respectively. , All are random numbers between 0 and 1. This represents the optimal individual position in the j-th iteration. Let be the position of the suboptimal solution at the j-th index;

[0070] During the optimization process, exploration and development modes are implemented, and a conversion mechanism is used to switch between the exploration phase and the development mode. The exploration mode includes a first exploration phase and a second exploration phase, and the development mode includes a first development phase and a second development phase. The transition between the first and second exploration phases is controlled, and the expression for the conversion mechanism is:

[0071] ,

[0072] In the formula, As the conversion factor, A random value between 0 and 1. For square root operators, and All are constants that change with the number of iterations; when When the value is greater than 1, the system is in exploration mode. Conversely, when... If the value is no greater than 1, switch to development mode.

[0073] Determine if the current iteration has reached the maximum number of iterations. If not, continue iterating; otherwise, stop iterating and output the optimal individual, which is the best grid connection parameter for the distributed photovoltaic system.

[0074] It should be noted that, to address the shortcomings of the exponential triangular optimization algorithm in the early stages of iteration, improvements were made to the first exploration phase. The improved expression is as follows:

[0075] ,

[0076] ,

[0077] ,

[0078] ,

[0079] ,

[0080] In the formula, This represents the j-th position of the i-th solution in the (t+1)-th iteration. This represents the j-th position of the i-th solution in the t-th iteration. A random number between 0 and 1 The first weighting coefficient of the candidate solution. The base is the natural number. , and Let t be a constant that varies with the number of iterations, where t represents the current iteration number. Indicates the maximum number of iterations. This represents the position of the worst-case individual in the t-th iteration. This represents the optimal individual position in the j-th iteration.

[0081] To address the inherent limitations of the exponential triangular optimization algorithm in terms of search accuracy, an elite error formula was introduced in the second development phase to improve it.

[0082] ,

[0083] ,

[0084] In the formula, As the worst individual globally, The constant coefficients, Let be the position of the suboptimal solution at the j-th index. The second weighting coefficient is the candidate solution.

[0085] To address the drawback of the exponential triangular optimization algorithm, which is prone to getting trapped in local optima in the later stages of iteration, a perturbation formula is introduced to help it escape local optima. Its expression is:

[0086] ,

[0087] ,

[0088] ,

[0089] In the formula, This represents the fitness value of the i-th individual. This represents the fitness value of any single individual in the population. and These are the worst fitness value and the best fitness value, respectively. It refers to the exploration area. This represents the position of a random individual in the j-th dimension after t+1 iterations. This represents the j-th position of the i-th solution in the (t+1)-th iteration. This represents the j-th position of the i-th solution in the t-th iteration. It is a random number between [0, 1]. It is the Euclidean distance between the i-th individual and the reference object. It is a random unit vector. This indicates the best location found in the current search. The mean position within the current population is represented by Levy, Levy represents the Levy distribution, rand represents a random value between 0 and 1, and t represents the current iteration number. This indicates the maximum number of iterations.

[0090] In this embodiment, by Figure 3 The comparison revealed that the improved exponential triangular optimization algorithm of this application achieves a better fitness.

[0091] In summary, the method of this application first establishes a small-signal mathematical model of a distributed photovoltaic power generation system, comprising photovoltaic cells, grid-connected inverters, transformers, and the power grid; secondly, it uses eigenvalue analysis to obtain the boundary values ​​of system parameters under stable conditions for each eigenvalue; then, it establishes optimization objective functions for small-disturbance stability, damping ratio, and stability margin; finally, it uses an improved exponential trigonometric optimization algorithm to optimize the objective functions, thereby determining the optimal grid-connected parameters of the distributed photovoltaic system. This significantly improves the grid-connected stability of the distributed photovoltaic system, thus ensuring its stable operation.

[0092] Please see Figure 4 The diagram shows a structural block diagram of a grid-connected parameter optimization system for a distributed photovoltaic system according to this application.

[0093] like Figure 4 As shown, the network parameter optimization system 200 includes a construction module 210, a determination module 220, an establishment module 230, and an optimization module 240.

[0094] The system includes a construction module 210 configured to construct a small-signal mathematical model of a distributed photovoltaic power generation system; a determination module 220 configured to obtain the system parameter boundary values ​​under stable conditions for each eigenvalue based on the small-signal mathematical model using eigenvalue analysis; an establishment module 230 configured to establish a coordinated optimization objective function for small-disturbance stability, damping ratio, and stability margin based on the system parameter boundary values; and an optimization module 240 configured to optimize the objective function using an improved exponential trigonometric optimization algorithm to obtain the optimal grid connection parameters for the distributed photovoltaic system.

[0095] It should be understood that Figure 4 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 4 The various modules in the document will not be described in detail here.

[0096] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the grid connection parameter optimization method for a distributed photovoltaic system in any of the above method embodiments.

[0097] In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0098] Constructing a small-signal mathematical model for a distributed photovoltaic power generation system;

[0099] Based on the small-signal mathematical model, the boundary values ​​of the system parameters under stable conditions for each eigenvalue are obtained by eigenvalue analysis.

[0100] Based on the boundary values ​​of the system parameters, establish a coordinated optimization objective function for small disturbance stability, damping ratio, and stability margin;

[0101] The improved exponential trigonometric optimization algorithm is used to optimize the objective function, thereby obtaining the optimal grid connection parameters for the distributed photovoltaic system.

[0102] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the grid connection parameter optimization system for the distributed photovoltaic system. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely configured relative to a processor, which can be connected to the grid connection parameter optimization system of the distributed photovoltaic system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0103] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 5 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 5 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the grid connection parameter optimization method for the distributed photovoltaic system described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the grid connection parameter optimization system for the distributed photovoltaic system. The output device 340 may include a display screen or other display device.

[0104] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0105] In one implementation, the above-described electronic device is applied to a grid connection parameter optimization system for a distributed photovoltaic system, serving as a client, and includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0106] Constructing a small-signal mathematical model for a distributed photovoltaic power generation system;

[0107] Based on the small-signal mathematical model, the boundary values ​​of the system parameters under stable conditions for each eigenvalue are obtained by eigenvalue analysis.

[0108] Based on the boundary values ​​of the system parameters, establish a coordinated optimization objective function for small disturbance stability, damping ratio, and stability margin;

[0109] The improved exponential trigonometric optimization algorithm is used to optimize the objective function, thereby obtaining the optimal grid connection parameters for the distributed photovoltaic system.

[0110] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing grid connection parameters of a distributed photovoltaic system, characterized in that, include: Constructing a small-signal mathematical model for a distributed photovoltaic power generation system; Based on the small-signal mathematical model, the boundary values ​​of the system parameters under stable conditions for each eigenvalue are obtained by eigenvalue analysis. Based on the boundary values ​​of the system parameters, establish a coordinated optimization objective function for small disturbance stability, damping ratio, and stability margin; The improved exponential trigonometric optimization algorithm is used to optimize the objective function, thereby obtaining the optimal grid connection parameters for the distributed photovoltaic system. Specifically, to address the shortcomings of the exponential trigonometric optimization algorithm in the initial iteration stage, improvements are made to the first exploration phase. The improved expression is as follows: ， ， ， ， ， In the formula, This represents the j-th position of the i-th solution in the (t+1)-th iteration. This represents the j-th position of the i-th solution in the t-th iteration. A random number between 0 and 1 The first weighting coefficient of the candidate solution. The natural base, , and Let t be a constant that varies with the number of iterations, where t represents the current iteration number. Indicates the maximum number of iterations. This represents the position of the worst-case individual in the t-th iteration. This represents the optimal individual position in the j-th iteration.

2. The method for optimizing grid connection parameters of a distributed photovoltaic system according to claim 1, characterized in that, The objective function for coordinating small-disturbance stability, damping ratio, and stability margin based on the system parameter boundary values ​​includes: The comprehensive optimization objective function, which considers the volatility, variability, and multiple operating scenarios of microgrid operations, is processed to obtain a coordinated optimization objective function. The expression of the coordinated optimization objective function is as follows: ， In the formula, To coordinate and optimize the objective function, Let be the objective function formed by the eigenvalues ​​with positive real parts in the distributed photovoltaic system. For distributed photovoltaic systems, the eigenvalue damping ratio is less than a given damping ratio. The objective function is composed of the eigenvalues ​​of . For a distributed photovoltaic system, the real part of the eigenvalues ​​is less than 0 and greater than a given real part. The objective function is composed of the eigenvalues.

3. The method for optimizing grid connection parameters of a distributed photovoltaic system according to claim 2, characterized in that, in, The objective function for calculating the eigenvalues ​​with positive real parts in a distributed photovoltaic system is expressed as follows: ， ， In the formula, Let be the real part of the i-th eigenvalue in the distributed photovoltaic system whose real part is greater than 0. The weighting coefficient for the i-th eigenvalue with a real part greater than 0 in a distributed photovoltaic system. Empirical parameters that are greater than 0; The eigenvalue damping ratio in a distributed photovoltaic system is less than a given damping ratio. The expression for the objective function formed by the eigenvalues ​​is: ， ， In the formula, For the j-th less than eigenvalue damping ratio, For the j-th less than The weighting coefficient of the eigenvalue damping ratio, The imaginary part of the j-th eigenvalue; In calculating the eigenvalues ​​of a distributed photovoltaic system, the real part is less than 0 and greater than a given real part. The expression for the objective function formed by the eigenvalues ​​is: ， ， In the formula, The real part of the k-th eigenvalue is less than 0 and greater than 0. The weighting coefficients of the real part of the eigenvalues, The real part of the k-th eigenvalue is less than 0 and greater than 0. The real part of the eigenvalues.

4. The method for optimizing grid connection parameters of a distributed photovoltaic system according to claim 1, characterized in that, The improved exponential triangular optimization algorithm is used to optimize the objective function, resulting in the optimal grid connection parameters for the distributed photovoltaic system, including: To address the inherent limitations of the exponential triangular optimization algorithm in terms of search accuracy, an elite error formula was introduced in the second development phase to improve it. ， ， In the formula, As the worst individual globally, The constant coefficients, Let be the position of the suboptimal solution at the j-th index. The second weighting coefficient for the candidate solution; To address the drawback of the exponential triangular optimization algorithm, which is prone to getting trapped in local optima in the later stages of iteration, a perturbation formula is introduced to help it escape local optima. Its expression is: ， ， ， In the formula, This represents the fitness value of the i-th individual. This represents the fitness value of any individual in the population. and These are the worst fitness value and the best fitness value, respectively. It refers to the exploration area. This represents the position of a random individual in the j-th dimension after t+1 iterations. This represents the j-th position of the i-th solution in the (t+1)-th iteration. This represents the j-th position of the i-th solution in the t-th iteration. It is a random number between [0, 1]. It is the Euclidean distance between the i-th individual and the reference object. It is a random unit vector. This indicates the best location found in the current search. The mean position within the current population is represented by Levy, Levy represents the Levy distribution, rand represents a random value between 0 and 1, and t represents the current iteration number. This indicates the maximum number of iterations.

5. A grid connection parameter optimization system for a distributed photovoltaic system, characterized in that, include: The module is configured to build a small-signal mathematical model for a distributed photovoltaic power generation system. The determination module is configured to obtain the boundary values ​​of system parameters under stable conditions for each eigenvalue based on the small-signal mathematical model using the eigenvalue analysis method. A module is established and configured to create a coordinated optimization objective function for small-disturbance stability, damping ratio, and stability margin based on the boundary values ​​of the system parameters. The optimization module is configured to use an improved exponential triangular optimization algorithm to optimize the objective function and obtain the optimal grid connection parameters for the distributed photovoltaic system. Specifically, to address the shortcomings of the exponential triangular optimization algorithm in the initial iteration stage, the first exploration phase is improved, and the improved expression is as follows: ， ， ， ， ， In the formula, This represents the j-th position of the i-th solution in the (t+1)-th iteration. This represents the j-th position of the i-th solution in the t-th iteration. A random number between 0 and 1 The first weighting coefficient of the candidate solution. The natural base, , and Let t be a constant that varies with the number of iterations, where t represents the current iteration number. Indicates the maximum number of iterations. This represents the position of the worst-case individual in the t-th iteration. This represents the optimal individual position in the j-th iteration.

6. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

7. 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 method described in any one of claims 1 to 4.

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