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

Abstract

The present application relates to the field of new energy grid-connected low-voltage fault ride-through technology, and specifically provides a new energy low-voltage ride-through parameter optimization method and device, comprising: obtaining current characteristic parameters affecting active and reactive current instruction generation during new energy low-voltage ride-through; taking the current characteristic parameters as initial values, using a particle swarm algorithm to iteratively optimize the characteristic parameters to obtain optimized characteristic parameters; wherein the characteristic parameters include at least one of the following: active current coefficient, reactive current coefficient, active current rise rate. The technical scheme provided by the present application combines electromagnetic transient simulation models with intelligent optimization algorithms to achieve optimization of low-voltage ride-through parameters, thereby achieving the best low-voltage fault ride-through effect of the power grid.

Description

Technical Field

[0001] This invention relates to the field of low-voltage fault ride-through technology for new energy grid connection, specifically to a method and apparatus for optimizing low-voltage ride-through parameters for new energy. Background Technology With the continuous commissioning of ultra-high-voltage direct current (UHVDC) transmission projects and the large-scale grid connection of renewable energy sources, the operating characteristics of the power system have undergone significant changes. The transient response characteristics of renewable energy sources under fault conditions have become a key factor affecting the safe and stable operation of the power grid. Currently, renewable energy units are generally required to possess low-voltage ride-through (LVRT) capability, that is, to provide active and reactive power to the grid during faults to support grid voltage. However, the parameter settings of the LVRT strategy directly determine the LVRT performance of renewable energy sources. Given that different grid structures and characteristics place varying requirements on the LVRT parameters of renewable energy units, the rationality of parameter design is crucial. Defective parameter design will not only weaken the support effect on grid voltage recovery during faults but may also worsen grid characteristics.

[0002] Currently, there are two main technical approaches to optimizing low-voltage ride-through parameters for renewable energy sources. The first is to construct a mechanistic model of the renewable energy unit and the power grid, using mathematical modeling and theoretical analysis to determine the basis for parameter selection. This method offers the highest accuracy, but it is typically only applicable to a single unit or a simple power grid system, resulting in a relatively narrow scope. The second approach involves building a simulation model of the renewable energy grid-connected system and searching for the optimal parameters through parameter traversal. While this method has broad applicability, it is time-consuming and labor-intensive, leading to lower parameter optimization efficiency.

[0003] For example, existing technologies propose a method and system for optimizing control parameters of new energy equipment. This method establishes a quasi-steady-state model of the entire system and uses gradient descent to optimize the parameters of the new energy equipment, thereby suppressing transient overvoltage problems caused by DC commutation failure. However, this method replaces each component of the power system with a quasi-steady-state model rather than an electromagnetic transient simulation model, thus affecting accuracy. Furthermore, it only addresses transient overvoltage suppression, limiting its applicability. Another example is a method and system for identifying parameters of new energy units in multiple test scenarios. This method constructs a simulation system corresponding to the test system and optimizes the equipment parameters of the simulation system using particle swarm optimization to ensure the results match those of the test system, thereby identifying the test system parameters. However, this method only identifies single-unit parameters and cannot optimize the control parameters of the new energy system during transient processes. Summary of the Invention

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

[0005] Firstly, a method for optimizing low-voltage ride-through parameters of renewable energy sources is provided, the method comprising: Obtain the current characteristic parameters that affect the generation of active and reactive current commands during the low-voltage ride-through of new energy sources; Using the current characteristic parameters as initial values, the particle swarm optimization algorithm is used to iteratively optimize the characteristic parameters to obtain optimized characteristic parameters; The characteristic parameters include at least one of the following: active current coefficient, reactive current coefficient, and active current rise rate.

[0006] Preferably, the active and reactive current commands are as follows:

[0007]

[0008] In the above formula, I dLVRT and I qLVRT For active and reactive current commands, K IdLV and K IqLV These are the active and reactive current coefficients. I N This is the rated current. I dp0 This represents the active current at the moment immediately preceding the fault. I Nmax This is the maximum current value. This is the voltage at the grid connection point.

[0009] Preferably, in the process of iterative optimization using the particle swarm optimization algorithm, the loss function is the sum of the fractions of the highest voltage, lowest voltage, high voltage area, low voltage area, and oscillation degree.

[0010] Furthermore, the scores for the highest voltage, lowest voltage, high voltage area, low voltage area, and oscillation degree are respectively 20 minus the fitness of the highest voltage, the fitness of the lowest voltage, the product of the high voltage area and its corresponding coefficient, the product of the low voltage area and its corresponding coefficient, and the fitness of the oscillation degree.

[0011] Preferably, in the process of iterative optimization using the particle swarm optimization algorithm, the loss function is the negative of the sum of the fitness of the highest voltage, the fitness of the lowest voltage, the product of the high voltage area and its corresponding coefficient, the product of the low voltage area and its corresponding coefficient, and the fitness of the oscillation degree.

[0012] Furthermore, the adaptability of the highest voltage is as follows:

[0013] In the above formula, The highest voltage evaluation coefficient is used. This is the highest voltage value. It is the second highest voltage evaluation coefficient.

[0014] Furthermore, the fitness of the minimum voltage is as follows:

[0015] In the above formula, The first lowest voltage evaluation coefficient, This is the lowest voltage value. This is the second lowest voltage evaluation coefficient.

[0016] Furthermore, the high-voltage area is the sum of all voltages exceeding the high-voltage fault threshold within the voltage sampling period, and the low-voltage area is the sum of all voltages below the low-voltage fault threshold within the voltage sampling period.

[0017] Furthermore, the fitness of the oscillation degree is as follows:

[0018] In the above formula, The coefficient representing the degree of oscillation. The number of sampling points skipped. This represents the voltage value at the (i+1)th sampling point.

[0019] Secondly, a device for optimizing low-voltage ride-through parameters for new energy sources is provided, the device comprising: The acquisition module is used to acquire the current characteristic parameters that affect the generation of active and reactive current commands during the low-voltage ride-through of new energy sources. The optimization module is used to iteratively optimize the feature parameters using the current feature parameters as initial values ​​and the particle swarm optimization algorithm to obtain optimized feature parameters. The characteristic parameters include at least one of the following: active current coefficient, reactive current coefficient, and active current rise rate.

[0020] Preferably, the active and reactive current commands are as follows:

[0021]

[0022] In the above formula, I dLVRT and I qLVRT For active and reactive current commands, K IdLVand K IqLV These are the active and reactive current coefficients. I N This is the rated current. I dp0 This represents the active current at the moment immediately preceding the fault. I Nmax This is the maximum current value. This is the voltage at the grid connection point.

[0023] Preferably, during the iterative optimization process using the particle swarm optimization algorithm, the loss function is the sum of the scores for the highest voltage, lowest voltage, high voltage area, low voltage area, and oscillation degree. Further, the scores for the highest voltage, lowest voltage, high voltage area, low voltage area, and oscillation degree are respectively 20 minus the fitness of the highest voltage, the fitness of the lowest voltage, the product of the high voltage area and its corresponding coefficient, the product of the low voltage area and its corresponding coefficient, and the fitness of the oscillation degree.

[0024] Preferably, in the process of iterative optimization using the particle swarm optimization algorithm, the loss function is the negative of the sum of the fitness of the highest voltage, the fitness of the lowest voltage, the product of the high voltage area and its corresponding coefficient, the product of the low voltage area and its corresponding coefficient, and the fitness of the oscillation degree.

[0025] Furthermore, the adaptability of the highest voltage is as follows:

[0026] In the above formula, The highest voltage evaluation coefficient is used. This is the highest voltage value. It is the second highest voltage evaluation coefficient.

[0027] Furthermore, the fitness of the minimum voltage is as follows:

[0028] In the above formula, The first lowest voltage evaluation coefficient, This is the lowest voltage value. This is the second lowest voltage evaluation coefficient.

[0029] Furthermore, the high-voltage area is the sum of all voltages exceeding the high-voltage fault threshold within the voltage sampling period, and the low-voltage area is the sum of all voltages below the low-voltage fault threshold within the voltage sampling period.

[0030] Furthermore, the fitness of the oscillation degree is as follows:

[0031] In the above formula, The coefficient representing the degree of oscillation. The number of sampling points skipped. This represents the voltage value at the (i+1)th sampling point.

[0032] 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 parameter optimization method is implemented.

[0033] 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 parameter optimization method is implemented.

[0034] 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 for optimizing low-voltage ride-through parameters for renewable energy sources, comprising: acquiring current characteristic parameters affecting the generation of active and reactive current commands during low-voltage ride-through; using the current characteristic parameters as initial values, iteratively optimizing the characteristic parameters using a particle swarm optimization algorithm to obtain optimized characteristic parameters; wherein the characteristic parameters include at least one of the following: active current coefficient, reactive current coefficient, and active current rise rate. The technical solution provided by this invention combines an electromagnetic transient simulation model with an intelligent optimization algorithm to optimize low-voltage ride-through parameters, thereby achieving the best low-voltage fault ride-through effect for the power grid. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the main steps of the new energy low voltage ride-through parameter optimization method according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the principle of a typical photovoltaic inverter control strategy according to an embodiment of the present invention; Figure 3 This is a receiving-end power grid topology diagram according to an embodiment of the present invention; Figure 4 This is the optimal fitness change curve obtained by using the method of the present invention in an embodiment of the present invention. Detailed Implementation

[0036] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0037] 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.

[0038] 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 parameter optimization method according to an embodiment of the present invention. Figure 1 As shown, the method for optimizing low-voltage ride-through parameters of new energy sources in this embodiment of the invention mainly includes the following steps: Step S101: Obtain the current characteristic parameters that affect the generation of active and reactive current commands during the low-voltage ride-through of new energy sources; Step S102: Using the current characteristic parameters as initial values, the particle swarm optimization algorithm is used to iteratively optimize the characteristic parameters to obtain optimized characteristic parameters; The characteristic parameters include at least one of the following: active current coefficient, reactive current coefficient, and active current rise rate.

[0039] In one implementation, the characteristic parameters can be a combination of any two of the active current coefficient, reactive current coefficient, and active current rise rate, or any one of the active current coefficient, reactive current coefficient, and active current rise rate, or the active current coefficient, reactive current coefficient, and active current rise rate.

[0040] In this embodiment, the schematic diagram of a typical photovoltaic inverter control strategy in step S101 is as follows: Figure 2 As shown, the DC power generated by new energy sources is converted into AC power that meets grid standards and connected to the grid through inversion, filtering, and precise control. At the same time, it can adapt to the asymmetrical operating conditions of the grid, ensuring the quality of grid-connected power and system stability. Figure 2 in, u dcref The reference value for DC bus voltage, u acref i is the reference value for the grid connection point voltage. dq i pq For the active current along the d-axis and q-axis, u dc This is the DC bus voltage.

[0041] The active and reactive current commands are as follows:

[0042]

[0043] In the above formula, I dLVRT and I qLVRT For active and reactive current commands, K IdLV and K IqLV These are the active and reactive current coefficients. I N This is the rated current. I dp0 This represents the active current at the moment immediately preceding the fault. I Nmax This is the maximum current value. This is the voltage at the grid connection point.

[0044] In this embodiment, the loss function used in the iterative optimization process employing the particle swarm optimization algorithm is as follows:

[0045] In the above formula, S Vmax , S Vmin , S AHV , S ALV and S osc These are the fractions for the highest voltage, lowest voltage, high voltage area, low voltage area, and oscillation degree, respectively. This represents the loss function value.

[0046] In one implementation, the fractions of the highest voltage, lowest voltage, high voltage area, low voltage area, and oscillation degree are as follows:

[0047] In the above formula, For the highest voltage adaptability, For the lowest voltage adaptability, For high voltage area, For low voltage area, For the degree of oscillation fitness, K HVA and K LVA The fitness coefficients for high-voltage area and low-voltage area are calculated.

[0048] In this embodiment, the loss function used in the iterative optimization process employing the particle swarm optimization algorithm is as follows:

[0049] In the above formula, For the highest voltage adaptability, For the lowest voltage adaptability, For high voltage area, For low voltage area, For the degree of oscillation fitness, K HVA and K LVA The fitness coefficients for high-voltage area and low-voltage area are calculated.

[0050] In one implementation, the adaptability of the highest voltage is as follows:

[0051] In the above formula, The highest voltage evaluation coefficient is used. This is the highest voltage value. It is the second highest voltage evaluation coefficient.

[0052] In one implementation, the fitness of the minimum voltage is as follows:

[0053] In the above formula, The first lowest voltage evaluation coefficient, This is the lowest voltage value. This is the second lowest voltage evaluation coefficient.

[0054] In one embodiment, the high-voltage area and the low-voltage area are as follows:

[0055] In the above formula, Let be the voltage value at the i-th sampling point. The sampling step size of the data. For high voltage fault threshold, The low voltage fault threshold, The sampling waveform period, This represents the total number of sampling points.

[0056] In one implementation, the oscillation fitness is as follows:

[0057] In the above formula, The coefficient representing the degree of oscillation. The number of sampling points skipped. This represents the voltage value at the (i+1)th sampling point.

[0058] In one specific implementation, a typical DC receiving-end power grid simulation model is built in RTDS. The simulation model topology is as follows: Figure 3 As shown. Active power recovery rate I dRate Active current coefficient K 2_Id_LV and reactive current coefficient K 1_Iq_LV The optimization ranges for the three parameters are [3,12], [0.3,1.25], and [1.3,2.25], respectively. First, a parameter traversal approach is used to find the optimal parameters. I dRate Points are taken at intervals of 0.5. K 2_Id_LV Points are taken at intervals of 0.05. K 1_Iq_LV Points were selected at intervals of 0.05, and the simulation parameters were evaluated using the loss function value of the particle swarm optimization algorithm. A total of 8000 simulations were required. After approximately 40 hours of simulation, the optimal parameters were found. I dRate =5.5、 K 2_Id_LV =1.0 and K 1_Iq_LV =1.8, with an optimal fitness of 196.78.

[0059] Next, the optimization method proposed in this invention is used, with the number of particles set to 10. Each particle consists of an active current coefficient, a reactive current coefficient, and an active current rise rate. After 100 iterations, a total of 1000 simulations are required. The simulation only takes about 5 hours, and the optimal parameters found are... I dRate =5.12、 K 2_Id_LV =1.09 and K 1_Iq_LV =1.64, with an optimal fitness of 169.33. Therefore, compared to parameter traversal methods, the method proposed in this invention not only significantly reduces the required time but also produces superior output results.

[0060] Figure 4 The curve showing the change of the optimal fitness during the iteration process of the method of the present invention is presented. Each particle consists of active current coefficient, reactive current coefficient and active current rise rate. It can be seen that the optimal output result is found at around the 50th generation, that is, the actual optimization time required is shorter than the set time.

[0061] Example 2 Based on the same inventive concept, the present invention also provides a new energy low-voltage ride-through parameter optimization device, the new energy low-voltage ride-through parameter optimization device comprising: The acquisition module is used to acquire the current characteristic parameters that affect the generation of active and reactive current commands during the low-voltage ride-through of new energy sources. The optimization module is used to iteratively optimize the feature parameters using the current feature parameters as initial values ​​and the particle swarm optimization algorithm to obtain optimized feature parameters. The characteristic parameters include at least one of the following: active current coefficient, reactive current coefficient, and active current rise rate.

[0062] Preferably, the active and reactive current commands are as follows:

[0063]

[0064] In the above formula, I dLVRT and I qLVRT For active and reactive current commands, K IdLV and K IqLV These are the active and reactive current coefficients. I N This is the rated current. I dp0 This represents the active current at the moment immediately preceding the fault. I Nmax This is the maximum current value. This is the voltage at the grid connection point.

[0065] Preferably, during the iterative optimization process using the particle swarm optimization algorithm, the loss function is as follows:

[0066] In the above formula, S Vmax , S Vmin , S AHV , S ALV and S osc These are the fractions for the highest voltage, lowest voltage, high voltage area, low voltage area, and oscillation degree, respectively. This represents the loss function value.

[0067] Furthermore, the fractions of the highest voltage, lowest voltage, high voltage area, low voltage area, and oscillation degree are as follows:

[0068] In the above formula, For the highest voltage adaptability, For the lowest voltage adaptability, For high voltage area, For low voltage area, For the degree of oscillation fitness, K HVA and K LVA The fitness coefficients for high-voltage area and low-voltage area are calculated.

[0069] Preferably, during the iterative optimization process using the particle swarm optimization algorithm, the loss function is as follows:

[0070] In the above formula, For the highest voltage adaptability, For the lowest voltage adaptability, For high voltage area, For low voltage area, For the degree of oscillation fitness, K HVA and K LVA The fitness coefficients for high-voltage area and low-voltage area are calculated.

[0071] Furthermore, the adaptability of the highest voltage is as follows:

[0072] In the above formula, The highest voltage evaluation coefficient is used. This is the highest voltage value. It is the second highest voltage evaluation coefficient.

[0073] Furthermore, the fitness of the minimum voltage is as follows:

[0074] In the above formula, The first lowest voltage evaluation coefficient, This is the lowest voltage value. This is the second lowest voltage evaluation coefficient.

[0075] Furthermore, the high-voltage area and the low-voltage area are as follows:

[0076] In the above formula, Let be the voltage value at the i-th sampling point. The sampling step size of the data. For high voltage fault threshold, The low voltage fault threshold, The sampling waveform period, This represents the total number of sampling points.

[0077] Furthermore, the fitness of the oscillation degree is as follows:

[0078] In the above formula, The coefficient representing the degree of oscillation. The number of sampling points skipped. This represents the voltage value at the (i+1)th sampling point.

[0079] 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 parameter optimization method in the above embodiments.

[0080] 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 parameter optimization method in the above embodiments.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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 low-voltage ride-through parameters in new energy sources, characterized in that, include: Obtain the current characteristic parameters that affect the generation of active and reactive current commands during the low-voltage ride-through of new energy sources; Using the current characteristic parameters as initial values, the particle swarm optimization algorithm is used to iteratively optimize the characteristic parameters to obtain optimized characteristic parameters; The characteristic parameters include at least one of the following: active current coefficient, reactive current coefficient, and active current rise rate.

2. The method as described in claim 1, characterized in that, The active and reactive current commands are as follows: In the above formula, I dLVRT and I qLVRT For active and reactive current commands, K IdLV and K IqLV These are the active and reactive current coefficients. I N This is the rated current. I dp0 This represents the active current at the moment immediately preceding the fault. I Nmax This is the maximum current value. This is the voltage at the grid connection point.

3. The method as described in claim 1, characterized in that, In the process of iterative optimization using the particle swarm optimization algorithm, the loss function is the sum of the fractions of the highest voltage, lowest voltage, high voltage area, low voltage area, and oscillation degree.

4. The method as described in claim 3, characterized in that, The scores for the highest voltage, lowest voltage, high voltage area, low voltage area, and oscillation degree are 20 minus the fitness of the highest voltage, the fitness of the lowest voltage, the product of the high voltage area and its corresponding coefficient, the product of the low voltage area and its corresponding coefficient, and the fitness of the oscillation degree.

5. The method as described in claim 1, characterized in that, In the process of iterative optimization using the particle swarm optimization algorithm, the loss function is the negative of the sum of the fitness of the highest voltage, the fitness of the lowest voltage, the product of the high voltage area and its corresponding coefficient, the product of the low voltage area and its corresponding coefficient, and the fitness of the oscillation degree.

6. The method according to any one of claims 1-5, characterized in that, The adaptability of the highest voltage is as follows: In the above formula, The highest voltage evaluation coefficient is used. This is the highest voltage value. It is the second highest voltage evaluation coefficient.

7. The method as described in claim 4 or 5, characterized in that, The fitness of the minimum voltage is as follows: In the above formula, The first lowest voltage evaluation coefficient, This is the lowest voltage value. This is the second lowest voltage evaluation coefficient.

8. The method according to any one of claims 1-5, characterized in that, The high-voltage area is the sum of all voltages exceeding the high-voltage fault threshold during the voltage sampling period, and the low-voltage area is the sum of all voltages below the low-voltage fault threshold during the voltage sampling period.

9. The method as described in claim 8, characterized in that, The oscillation fitness is as follows: In the above formula, The coefficient representing the degree of oscillation. The number of sampling points skipped. This represents the voltage value at the (i+1)th sampling point.

10. A device for optimizing low-voltage ride-through parameters in new energy sources, characterized in that, include: The acquisition module is used to acquire the current characteristic parameters that affect the generation of active and reactive current commands during the low-voltage ride-through of new energy sources. The optimization module is used to iteratively optimize the feature parameters using the current feature parameters as initial values ​​and the particle swarm optimization algorithm to obtain optimized feature parameters. The characteristic parameters include at least one of the following: active current coefficient, reactive current coefficient, and active current rise rate.

11. The apparatus as claimed in claim 9, characterized in that, The active and reactive current commands are as follows: In the above formula, I dLVRT and I qLVRT For active and reactive current commands, K IdLV and K IqLV These are the active and reactive current coefficients. I N This is the rated current. I dp0 This represents the active current at the moment immediately preceding the fault. I Nmax This is the maximum current value. This is the voltage at the grid connection point.

12. The apparatus as claimed in claim 9, characterized in that, In the process of iterative optimization using the particle swarm optimization algorithm, the loss function is the sum of the fractions of the highest voltage, lowest voltage, high voltage area, low voltage area, and oscillation degree.

13. The apparatus as claimed in claim 12, characterized in that, The scores for the highest voltage, lowest voltage, high voltage area, low voltage area, and oscillation degree are 20 minus the fitness of the highest voltage, the fitness of the lowest voltage, the product of the high voltage area and its corresponding coefficient, the product of the low voltage area and its corresponding coefficient, and the fitness of the oscillation degree.

14. The apparatus as claimed in claim 9, characterized in that, In the process of iterative optimization using the particle swarm optimization algorithm, the loss function is the negative of the sum of the fitness of the highest voltage, the fitness of the lowest voltage, the product of the high voltage area and its corresponding coefficient, the product of the low voltage area and its corresponding coefficient, and the fitness of the oscillation degree.

15. The apparatus as claimed in claim 13 or 14, characterized in that, The adaptability of the highest voltage is as follows: In the above formula, The highest voltage evaluation coefficient is used. This is the highest voltage value. It is the second highest voltage evaluation coefficient.

16. The apparatus as claimed in claim 13 or 14, characterized in that, The fitness of the minimum voltage is as follows: In the above formula, The first lowest voltage evaluation coefficient, This is the lowest voltage value. This is the second lowest voltage evaluation coefficient.

17. The apparatus as claimed in claim 13 or 14, characterized in that, The high-voltage area is the sum of all voltages exceeding the high-voltage fault threshold during the voltage sampling period, and the low-voltage area is the sum of all voltages below the low-voltage fault threshold during the voltage sampling period.

18. The apparatus as claimed in claim 17, characterized in that, The oscillation fitness is as follows: In the above formula, The coefficient representing the degree of oscillation. The number of sampling points skipped. This represents the voltage value at the (i+1)th sampling point.

19. 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 parameter optimization method as described in any one of claims 1 to 9 is implemented.

20. A computer-readable storage medium, characterized in that, It contains a computer program, which, when executed, implements the new energy low voltage ride-through parameter optimization method as described in any one of claims 1 to 9.

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