A new energy low voltage ride through control parameter optimization method and device

Abstract

The present application relates to the technical field of new energy power generation control, and specifically provides a new energy low-voltage ride-through control parameter optimization method and device, comprising: solving a pre-constructed new energy low-voltage ride-through control parameter optimization model to obtain an optimization result; based on the optimization result, obtaining a new energy low-voltage ride-through control parameter optimization scheme; wherein the optimization result is a control parameter vector of each new energy station. The technical scheme provided by the present application realizes automatic optimization of control parameters and provides a basis for control parameter setting of weak areas of a receiving end power grid.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation control technology, specifically to a method and device for optimizing control parameters for low voltage ride-through in new energy sources. Background Technology With the large-scale integration of new energy sources and the continuous expansion of DC transmission, the receiving-end power grid is gradually exhibiting operating characteristics of high proportion of new energy, high proportion of power electronic equipment, multiple DC feeds, and highly concentrated loads. In such systems, the active and reactive current commands of new energy inverters during low-voltage ride-through directly affect their power output characteristics during fault periods. Under different control parameters, the inverter's active power recovery speed, reactive current injection level, and capacity allocation relationship after current limiting will all change, thereby altering the system's active power deficit, reactive power support capacity, and power flow transfer degree after a fault, ultimately affecting the transient voltage recovery process of the receiving-end power grid.

[0002] Existing low-voltage ride-through control parameters for new energy inverters are typically determined based on engineering experience, typical parameter sets, or single-parameter scanning. While these methods are simple to implement, they have the following shortcomings: 1) Manual trial-and-error methods rely on experience and are difficult to adapt to the parameter tuning requirements of receiving-end power grids with multiple sites, regions, and operating conditions; 2) Single-parameter scanning can only reflect the impact of a single parameter change on the system's transient response, making it difficult to obtain an optimal combination of multiple control parameters; 3) When only a single transient indicator such as minimum voltage, recovery time, or voltage deviation is used as the optimization target, it is difficult to comprehensively reflect the overall transient voltage support capability of the receiving-end power grid; 4) Existing parameter optimization methods are usually geared towards uniform adjustments across the entire grid, lacking a targeted parameter optimization mechanism for voltage-weak areas.

[0003] Therefore, it is necessary to study the optimization method of low-voltage ride-through control parameters for new energy sources, so as to provide a basis for the tuning of control parameters in weak areas of the receiving-end power grid. Summary of the Invention

[0004] To overcome the above-mentioned shortcomings, this invention proposes a method and device for optimizing control parameters for low-voltage ride-through in new energy sources.

[0005] Firstly, a method for optimizing control parameters for low-voltage ride-through of renewable energy sources is provided, the method comprising: Solve the pre-built optimization model of low-voltage ride-through control parameters for new energy sources to obtain the optimization results; Based on the optimization results, an optimized control parameter scheme for low-voltage ride-through of new energy sources is obtained. The optimization result is the control parameter vector for each new energy power station.

[0006] Preferably, the pre-constructed new energy low-voltage ride-through control parameter optimization model is solved using the particle swarm optimization algorithm.

[0007] Preferably, the pre-constructed new energy low-voltage ride-through control parameter optimization model includes: an objective function with the short-circuit ratio of multiple new energy stations in the receiving-end power grid as the objective and its corresponding constraints.

[0008] Furthermore, the objective function is as follows:

[0009] In the above formula, For the feasible region of the parameters, The objective function value, Control parameter vectors for each new energy power station Corresponding to the short-circuit ratio of multiple renewable energy power plants in the receiving-end power grid, This represents the control parameter vector for each new energy power station.

[0010] Furthermore, the control parameter vectors of each new energy power station The process of obtaining the short-circuit ratio of multiple renewable energy power plants in the receiving-end power grid includes: Based on the control parameter vectors of each new energy power station Perform electromagnetic transient simulation to obtain simulation data; The short-circuit ratio of multiple renewable energy power stations in the receiving-end power grid was calculated using the simulation data.

[0011] Furthermore, the constraints include: control parameter constraints, node voltage constraints, power balance constraints, branch power constraints, and distribution network radial operation constraints.

[0012] Furthermore, the constraints are as follows: x d,min ≤ x d ≤ x d,max

[0013]

[0014]

[0015] In the above formula, x d,min For the first d The lower limit of control parameters for new energy power plants that need to be optimized. x d For the first d One set of control parameters for new energy power plants that need to be optimized. x d,max For the first d The upper limit of control parameters for new energy power stations needs to be optimized.U i,t For nodes i During the period voltage value, U min For nodes i The lower limit of voltage, U max For nodes i The upper limit of voltage, P i,t For nodes i During the period t Injected active power, Q i,t For nodes i During the period t Injected reactive power, G ij For the conductance between nodes i and j, B ij For nodes i, j The susceptance between δ ij,t For nodes i , j During the period t Voltage phase angle difference, n The total number of system nodes. U j,t For nodes j During the period t voltage value, P j,t branch road j During the period t The active power value, Let be the maximum allowable active power of branch j.

[0016] Secondly, a device for optimizing low-voltage ride-through control parameters for new energy sources is provided, the device comprising: The first analysis module is used to solve the pre-built optimization model of low-voltage ride-through control parameters for new energy sources and obtain the optimization results. The second analysis module is used to obtain an optimized scheme for the low-voltage ride-through control parameters of new energy sources based on the optimization results. The optimization result is the control parameter vector for each new energy power station.

[0017] Thirdly, a computer device is provided, comprising: one or more processors; The processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the new energy low voltage ride-through control parameter optimization method is implemented.

[0018] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, wherein when the computer program is executed, the new energy low voltage ride-through control parameter optimization method is implemented.

[0019] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects: This invention provides a method and apparatus for optimizing low-voltage ride-through control parameters for renewable energy sources, comprising: solving a pre-constructed optimization model for low-voltage ride-through control parameters of renewable energy sources to obtain optimization results; and obtaining an optimization scheme for low-voltage ride-through control parameters of renewable energy sources based on the optimization results; wherein the optimization results are control parameter vectors for each renewable energy power station. The technical solution provided by this invention uses the short-circuit ratio of multiple renewable energy power stations in the receiving-end power grid as the optimization target for the control parameters of renewable energy power stations. This enables the establishment of a quantitative link between changes in the control parameters of renewable energy power stations and the overall transient voltage support capability of the receiving-end power grid, avoiding the limitations of relying solely on single-node voltage indicators for parameter tuning. Furthermore, this invention uses a particle swarm optimization algorithm to jointly optimize the control parameters of renewable energy power stations, automatically searching for optimal parameter combinations under multi-parameter coupling conditions, reducing the workload of manual trial and error and repetitive simulations. The obtained optimized parameters can improve the transient voltage recovery process after a fault in the receiving-end power grid, enhance the transient voltage support capability of high-proportion renewable energy access to the receiving-end power grid, and provide a basis for the tuning of control parameters of renewable energy power stations and the management of vulnerable areas. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the main steps of the new energy low voltage ride-through control parameter optimization method according to an embodiment of the present invention; Figure 2 This is an embodiment of the present invention. K 2_Ip_LV and Ip set_LV In-plane particle search distribution diagram; Figure 3 This is an embodiment of the present invention. K 1_Ip_LV and K 1_Iq_LV In-plane particle search distribution diagram; Figure 4 This is an iterative change curve of the short-circuit ratio of multiple new energy power stations in the receiving-end power grid during the particle swarm optimization process of this invention embodiment; Figure 5 This is a comparison diagram of the transient voltage response before and after optimization in an embodiment of the present invention. Detailed Implementation

[0021] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

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

[0023] Example 1 See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a new energy low-voltage ride-through control parameter optimization method according to an embodiment of the present invention. Figure 1 As shown, the method for optimizing control parameters for low-voltage ride-through of new energy sources in this embodiment of the invention mainly includes the following steps: Step S101: Solve the pre-built optimization model of low-voltage ride-through control parameters for new energy sources to obtain the optimization results; Step S102: Based on the optimization results, obtain the optimized control parameter scheme for low voltage ride-through of new energy sources; The optimization results are the control parameter vectors for each new energy power station. The control parameters of the new energy power station that need to be optimized can be determined based on the control model of the new energy power station, the engineering operation requirements, and the transient voltage support requirements of the receiving-end power grid.

[0024] During the optimization process, the control parameters of the new energy power stations that need to be tuned are optimized, while the typical values ​​of the control parameters that do not need to be adjusted or are not involved in the optimization are kept unchanged, so as to reduce the optimization dimensionality and improve the optimization efficiency.

[0025] In this embodiment, the pre-constructed new energy low-voltage ride-through control parameter optimization model is solved using the particle swarm optimization algorithm.

[0026] In this embodiment, the pre-constructed optimization model for low-voltage ride-through control parameters of new energy sources includes: an objective function with the short-circuit ratio of multiple new energy stations in the receiving-end power grid as the objective and its corresponding constraints.

[0027] In one implementation, the objective function is as follows:

[0028] In the above formula, For the feasible region of the parameters, The objective function value, Control parameter vectors for each new energy power station Corresponding to the short-circuit ratio of multiple renewable energy power plants in the receiving-end power grid, This represents the control parameter vector for each new energy power station.

[0029] In one implementation, the control parameter vectors of each new energy power station The process of obtaining the short-circuit ratio of multiple renewable energy power plants in the receiving-end power grid includes: Based on the control parameter vectors of each new energy power station Perform electromagnetic transient simulation to obtain simulation data; The short-circuit ratio of multiple renewable energy power stations in the receiving-end power grid was calculated using the simulation data.

[0030] In one implementation, the constraints include: control parameter constraints, node voltage constraints, power balance constraints, branch power constraints, and distribution network radial operation constraints.

[0031] In one implementation, the constraints are as follows: x d,min ≤ x d ≤ x d,max

[0032]

[0033]

[0034] In the above formula, x d,min For the first d The lower limit of control parameters for new energy power plants that need to be optimized. x d For the first d One set of control parameters for new energy power plants that need to be optimized. x d,max For the first d The upper limit of control parameters for new energy power stations needs to be optimized. U i,t For nodes i During the period voltage value, U min For nodes i The lower limit of voltage, U max For nodes i The upper limit of voltage, P i,t For nodes i During the period t Injected active power, Q i,t For nodes i During the period t Injected reactive power, G ijFor the conductance between nodes i and j, B ij For nodes i, j The susceptance between δ ij,t For nodes i , j During the period t Voltage phase angle difference, n The total number of system nodes. U j,t For nodes j During the period t voltage value, P j,t branch road j During the period t The active power value, Let be the maximum allowable active power of branch j.

[0035] In one specific implementation, the initial values ​​of the low-voltage ride-through control parameters for the new energy inverter are as follows: K 1_Ip_LV =0; K 2_Ip_LV =0; I pset_LV =0.1; K 1_Iq_LV =1.5; K 2_Iq_LV =1; I qset_LV =0; the fault is set to a three-phase ground fault, and the fault duration is 0.1s. This embodiment uses... K 1_Ip_LV , K 2_Ip_LV , I pset_LV , K 1_Iq_LV The optimization object has a parameter dimension of 4.

[0036] The control logic, current limiting circuit, low-voltage ride-through control logic, and related protection mechanisms for the new energy power station have been implemented in the RTDS time-domain simulation model, and no additional redundant constraints are set during the optimization process. For particles that exceed the feasible region boundary during the iteration process, a boundary truncation method is used to handle them, that is, the out-of-bounds parameters are corrected to the corresponding boundary values.

[0037] The constraints set in this embodiment are as follows: ; The particle swarm size, maximum number of iterations, inertia weight, individual learning factor, social learning factor, initial velocity, and maximum velocity are set. The initial particle positions are randomly generated within the feasible region of the control parameters for each new energy power station. The initial velocity is randomly assigned according to a certain proportion of the search interval width for each parameter dimension. To prevent particles from crossing excessively large ranges during iteration, which would reduce the effective search accuracy, particle velocities are limited, and the maximum velocity for each parameter dimension is set according to a certain proportion of the corresponding search interval width.

[0038] Inertia weights can be fixed or decreased. When using a decreasing strategy, a larger inertia weight is maintained in the early stages of optimization to improve global search capabilities; the inertia weight is reduced in the later stages of optimization to enhance local optimization capabilities.

[0039] In this embodiment, the particle swarm size is set to 20, the maximum number of iterations is 20, and the inertia weight adopts a linear decreasing strategy, with an initial value of [value missing]. ω max =0.9, the termination value is taken as 0.9. ω min =0.4, individual learning factor and social learning factor respectively take c 1= c 2 = 1.5. The initial particle position is randomly generated within the feasible region of each parameter. The initial velocity is randomly given according to 20% of the search interval width of each parameter dimension. To avoid the particle crossing too large a range during iteration and reducing the effective search accuracy, the particle velocity is limited. d The maximum speed of the dimension parameter is set to 30% of the width of the corresponding search interval.

[0040] A workflow for optimizing control parameters of renewable energy power plants was established, integrating Python, RSCAD, and RTDS. The Python program is responsible for iterative optimization using the particle swarm optimization algorithm, generating control parameters, scheduling simulation tasks, and calculating the short-circuit ratio of multiple renewable energy power plants in the receiving-end grid. RSCAD receives script commands from the Python program and completes the RTDS model operation control, parameter writing, and data export. RTDS performs electromagnetic transient simulations based on different combinations of control parameters for renewable energy power plants and feeds the simulation data back to RSCAD. The Python program then reads the simulation results and uses them for subsequent fitness calculations.

[0041] For a given operating condition and network topology, the regional source-load balance correction factor and DC correction factor in the calculation of the short-circuit ratio of multiple renewable energy power stations in the receiving-end power grid are mainly determined by the grid structure, regional source-load distribution, and DC landing point location. These factors remain unchanged during the optimization process and serve as fixed inputs. The particle swarm optimization algorithm optimizes the control parameters of the renewable energy power stations. Changes in these parameters are reflected in the transient support efficiency correction factor of the renewable energy power stations through transient simulation results, ultimately affecting the short-circuit ratio of multiple renewable energy power stations in the receiving-end power grid.

[0042] For each particle in the current iteration, perform the following operation: (1) Read the control parameter combination of the new energy power station corresponding to the current particle; (2) Write the control parameters into the RSCAD / RTDS simulation model using a Python program; (3) Perform fault transient simulation in RTDS to obtain transient response data under this combination of control parameters; (4) The Python program reads the simulation results and calculates the short-circuit ratio of the new energy multi-stations in the receiving-end power grid corresponding to the parameter combination; (5) The short-circuit ratio of new energy power stations at the receiving end of the power grid is used as the fitness value of the particle; (6) Compare the current fitness value with the historical best fitness value of the particle. If the current short-circuit ratio of new energy multi-site in the receiving-end power grid is higher than the historical best value of the particle, then update the individual's best position. (7) Compare the current fitness value with the historical best fitness value of the particle swarm. If the current short-circuit ratio of new energy power stations in the receiving end of the grid is higher than the historical best value of the particle swarm, then update the global best position. (8) Generate the next-generation new energy power station control parameter combination based on the particle swarm velocity and position update formula; (9) Perform boundary correction on particles that exceed the feasible region and limit their velocity; (10) Determine whether the maximum number of iterations has been reached. If not, return to step (1) to continue iterating. If it has been reached, stop the optimization and output the combination of control parameters of the new energy power station corresponding to the global optimal particle.

[0043] A receiving-end power grid example is constructed, which includes a multi-regional receiving-end AC grid, multiple renewable energy power plants, multiple DC landing points, and corresponding load models. The renewable energy power plants include centralized renewable energy, distributed renewable energy, and other renewable energy sources. Among them, the centralized renewable energy inverter adopts low-voltage ride-through control and is used as the control parameter optimization object in this embodiment.

[0044] An electromagnetic transient simulation model of the receiving-end power grid was built in RTDS, and the system operating mode, DC transmission power, renewable energy output level, load ratio, fault location, fault type, and fault clearing time were set. During the optimization process, the system grid structure, operating conditions, fault settings, and non-optimized control parameters were kept constant to ensure the comparability of simulation results under different parameter combinations. After 20 iterations of optimization, the globally optimal short-circuit ratio of renewable energy multiple power stations in the receiving-end power grid was obtained as 2.244861. In the embodiment... K 2_Ip_LV and Ipset_LV In-plane particle search distribution diagram as follows Figure 2 As shown in the embodiment K 1_Ip_LV and K 1_Iq_LV In-plane particle search distribution diagram as follows Figure 3 As shown, the iterative change curves of the short-circuit ratio of multiple renewable energy power plants in the receiving-end grid during the particle swarm optimization process are as follows: Figure 4 As shown.

[0045] The corresponding optimized parameter sets are shown in Table 1: Table 1

[0046] To verify the effectiveness of the optimized parameter set, the low-voltage ride-through control parameters of all new energy inverters in the entire network were set to typical parameter set one, typical parameter set two, and the optimized parameter set, respectively, and fault transient simulations were performed for each set. The three sets of parameters are shown in Table 2: Table 2

[0047] Comparison of results before and after optimization in the examples Figure 5 As shown in the figure. Simulation results show that when using typical parameter group one, the node voltage failed to recover effectively after the fault was cleared, and there was a continuous low voltage and obvious oscillation. When using typical parameter group two, the node voltage recovery process was improved compared with typical parameter group one. When using the optimized parameter group, the recovery speed and recovery level of the fault node voltage were further improved, indicating that the optimized parameter group can improve the transient voltage recovery process after the receiving end of the power grid fault.

[0048] Example 2 Based on the same inventive concept, the present invention also provides a new energy low-voltage ride-through control parameter optimization device, the new energy low-voltage ride-through control parameter optimization device comprising: The first analysis module is used to solve the pre-built optimization model of low-voltage ride-through control parameters for new energy sources and obtain the optimization results. The second analysis module is used to obtain an optimized scheme for the low-voltage ride-through control parameters of new energy sources based on the optimization results. The optimization result is the control parameter vector for each new energy power station.

[0049] In this embodiment, the pre-constructed new energy low-voltage ride-through control parameter optimization model is solved using the particle swarm optimization algorithm.

[0050] In this embodiment, the pre-constructed optimization model for low-voltage ride-through control parameters of new energy sources includes: an objective function with the short-circuit ratio of multiple new energy stations in the receiving-end power grid as the objective and its corresponding constraints.

[0051] In one implementation, the objective function is as follows:

[0052] In the above formula, For the feasible region of the parameters, The objective function value, Control parameter vectors for each new energy power station Corresponding to the short-circuit ratio of multiple renewable energy power plants in the receiving-end power grid, This represents the control parameter vector for each new energy power station.

[0053] In one implementation, the control parameter vectors of each new energy power station The process of obtaining the short-circuit ratio of multiple renewable energy power plants in the receiving-end power grid includes: Based on the control parameter vectors of each new energy power station Perform electromagnetic transient simulation to obtain simulation data; The short-circuit ratio of multiple renewable energy power stations in the receiving-end power grid was calculated using the simulation data.

[0054] In one implementation, the constraints include: control parameter constraints, node voltage constraints, power balance constraints, branch power constraints, and distribution network radial operation constraints.

[0055] In one implementation, the constraints are as follows: x d,min ≤ x d ≤ x d,max

[0056]

[0057]

[0058] In the above formula, x d,min For the first d The lower limit of control parameters for new energy power plants that need to be optimized. x d For the first d One set of control parameters for new energy power plants that need to be optimized. x d,max For the first d The upper limit of control parameters for new energy power stations needs to be optimized. U i,t For nodes i During the period voltage value, U minFor nodes i The lower limit of voltage, U max For nodes i The upper limit of voltage, P i,t For nodes i During the period t Injected active power, Q i,t For nodes i During the period t Injected reactive power, G ij For the conductance between nodes i and j, B ij For nodes i, j The susceptance between δ ij,t For nodes i , j During the period t Voltage phase angle difference, n The total number of system nodes. U j,t For nodes j During the period t voltage value, P j,t branch road j During the period t The active power value, Let be the maximum allowable active power of branch j.

[0059] Example 3 Based on the same inventive concept, this invention also provides a computer device, which includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement corresponding method flows or corresponding functions, thereby realizing the steps of the new energy low-voltage ride-through control parameter optimization method in the above embodiments.

[0060] Example 4 Based on the same inventive concept, this invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the new energy low-voltage ride-through control parameter optimization method in the above embodiments.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing control parameters for low-voltage ride-through in new energy sources, characterized in that, The method includes: Solve the pre-built optimization model of low-voltage ride-through control parameters for new energy sources to obtain the optimization results; Based on the optimization results, an optimized scheme for the low-voltage ride-through control parameters of new energy sources is obtained. The optimization result is the control parameter vector for each new energy power station.

2. The method as described in claim 1, characterized in that, The pre-constructed optimization model for low-voltage ride-through control parameters of new energy sources is solved using the particle swarm optimization algorithm.

3. The method as described in claim 1, characterized in that, The pre-constructed optimization model for low-voltage ride-through control parameters of new energy sources includes: an objective function with the short-circuit ratio of multiple new energy stations in the receiving-end power grid as the objective and its corresponding constraints.

4. The method as described in claim 3, characterized in that, The objective function is as follows: In the above formula, For the feasible region of the parameters, The objective function value, Control parameter vectors for each new energy power station Corresponding to the short-circuit ratio of multiple renewable energy power plants in the receiving-end power grid, This represents the control parameter vector for each new energy power station.

5. The method as described in claim 4, characterized in that, The control parameter vector of each new energy power station The process of obtaining the short-circuit ratio of multiple renewable energy power plants in the receiving-end power grid includes: Based on the control parameter vectors of each new energy power station Perform electromagnetic transient simulation to obtain simulation data; The short-circuit ratio of multiple renewable energy power stations in the receiving-end power grid was calculated using the simulation data.

6. The method as described in claim 4, characterized in that, The constraints include: control parameter constraints, node voltage constraints, power balance constraints, branch power constraints, and distribution network radial operation constraints.

7. The method as described in claim 6, characterized in that, The constraints are as follows: x d,min ≤ x d ≤ x d,max In the above formula, x d,min For the first d The lower limit of control parameters for new energy power plants that need to be optimized. x d For the first d One set of control parameters for new energy power plants that need to be optimized. x d,max For the first d The upper limit of control parameters for new energy power plants needs to be optimized. U i,t For nodes i During the period voltage value, U min For nodes i The lower limit of voltage, U max For nodes i The upper limit of voltage, P i,t For nodes i During the period t Injected active power, Q i,t For nodes i During the period t Injected reactive power, G ij For the conductance between nodes i and j, B ij For nodes i, j The susceptance between δ ij,t For nodes i , j During the period t Voltage phase angle difference, n This represents the total number of system nodes. U j,t For nodes j During the period t voltage value, P j,t branch road j During the period t The active power value, Let be the maximum allowable active power of branch j.

8. An apparatus based on the new energy low-voltage ride-through control parameter optimization method according to any one of claims 1-7, characterized in that, The device includes: The first analysis module is used to solve the pre-built optimization model of low-voltage ride-through control parameters for new energy sources and obtain the optimization results. The second analysis module is used to obtain an optimized scheme for the low-voltage ride-through control parameters of new energy sources based on the optimization results. The optimization result is the control parameter vector for each new energy power station.

9. A computer device, characterized in that, include: One or more processors; The processor is used to execute one or more programs; When the one or more programs are executed by the one or more processors, the new energy low voltage ride-through control parameter optimization method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the new energy low voltage ride-through control parameter optimization method as described in any one of claims 1 to 7.

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