Parameter setting-based inverter low-penetration performance optimization method for wind, light and fire base

Abstract

The invention discloses a method for optimizing the low-penetration performance of an inverter of a wind-solar-fire base based on parameter setting, and belongs to the technical field of inverter parameter setting. On the basis of a wind turbine generator and photovoltaic inverter grid-related voltage control performance analysis formula, control parameters influencing low-voltage ride-through performance are analyzed, and final to-be-optimized key control parameters are determined; establishing a new energy inverter low-voltage ride-through key control parameter optimization setting model by taking the minimum grid-connected voltage fluctuation range of the new energy station as an objective function and combining the actual operation range of the final to-be-optimized key control parameter as a constraint condition; and solving the setting model by using a particle swarm optimization algorithm to obtain optimal key control parameters of the low-voltage ride-through control performance of the new energy inverter. By setting the control parameters of the inverter, the low-voltage ride-through level of the new energy station is improved, and the off-network risk of the new energy station caused by power grid faults is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of inverter parameter setting, and in particular to a method for optimizing the low-breakdown performance of an inverter of a wind-solar-thermal base based on parameter setting. Background Art

[0002] Traditional power sources such as thermal power and hydropower have strong voltage support capabilities and can provide reliable support when the system fails or fluctuates. However, in bases with a high proportion of new energy, the proportion of traditional power sources is low and cannot effectively cope with sudden voltage fluctuations. In wind, solar and thermal energy bases, the intermittent and volatile nature of new energy generation increases the complexity of grid operation, especially when the grid fails, the low voltage ride-through capability of new energy inverters becomes the key to system stability. Low voltage ride-through technology requires that new energy units can continue to connect to the grid when the grid voltage drops suddenly, rather than disconnecting from the grid due to voltage fluctuations. However, the low voltage ride-through capability of wind power and photovoltaic inverters is affected by many factors, especially the setting of their control parameters, which directly determines their response effect during the low voltage ride-through process. If the control parameters are set improperly, the new energy inverter may not be able to make effective adjustments in time when the grid voltage fluctuates, thereby increasing the risk of disconnection and affecting the safety and stability of the entire grid.

[0003] To this end, the present invention proposes a method for optimizing the low-throughput performance of inverters of wind, solar and thermal bases based on parameter setting to solve the problems that urgently need to be solved in the above-mentioned new energy field. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the traditional parameter setting methods in the prior art that lack consideration of complex operating scenarios and various fault types, and it is difficult to find the optimal parameters that meet various operating conditions. The new energy inverter may not be able to make effective adjustments in time when the grid voltage fluctuates, increasing the risk of grid disconnection. The proposed inverter low-throughput performance optimization method for wind, solar and thermal bases based on parameter setting fully considers complex operating scenarios and various fault types, finds the optimal parameters that meet various operating conditions, improves the transient stability of the voltage of the new energy base, and ensures the safety of the grid.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for optimizing the low-throughput performance of an inverter of a wind-solar-thermal base based on parameter setting, the method comprising the following steps:

[0007] Step S1: Based on the grid-related voltage control performance analysis formula of wind turbines and photovoltaic inverters, analyze the control parameters that affect the low voltage ride-through performance and determine the final key control parameters to be optimized;

[0008] Step S2: Taking the minimum voltage fluctuation range of the new energy station as the objective function and combining the actual operating range of the key control parameters to be optimized as the constraint condition, a setting model for optimizing the key control parameters of the new energy inverter low voltage ride-through is created;

[0009] In step S3, for the fault scenario, the particle swarm optimization algorithm is used to solve the tuning model and obtain the optimal key control parameters for the low voltage ride-through control performance of the new energy inverter.

[0010] Furthermore, in step S1, the grid-related voltage control performance of the wind turbine and photovoltaic inverter is determined by the active current command and reactive current command in the low-voltage ride-through project of the new energy inverter. Based on the grid-related voltage control performance analysis formula of the wind turbine and photovoltaic inverter, the current control command expression of the wind turbine inverter and the current control command expression of the photovoltaic inverter are used to express it.

[0011] The current control command expression of the wind turbine inverter is:

[0012] ;

[0013] In the above formula, and They represent the active current command and reactive current command of the power output in the low voltage ride-through project of the new energy inverter. and is the active current calculation coefficient of low voltage ride through in the active current control instruction expression of the wind turbine inverter, The active current setting value for low voltage ride-through of the wind turbine inverter. and is the reactive current calculation coefficient of the low voltage ride through of the reactive current control instruction expression of the wind turbine inverter, Indicates the voltage value at which the wind turbine inverter enters low voltage ride-through. is the initial active current of the wind turbine inverter, and 0.9 is the voltage threshold for the wind turbine inverter to enter low voltage ride-through; is the initial reactive current of the wind turbine inverter; Set the reactive current value for low voltage ride-through of wind turbine inverter;

[0014] The current control command expression of the photovoltaic inverter is:

[0015] ;

[0016] In the formula, Indicates the active current command during the low voltage ride-through control process of the photovoltaic inverter; Indicates the reactive current command during the low voltage ride through control process of the photovoltaic inverter; Indicates the voltage of the photovoltaic cluster grid connection point detected during the low voltage drop process; Indicates the active power reference value of the PV cluster before the low voltage drops; Indicates the maximum current output by the photovoltaic inverter. It is the reactive current value of low voltage ride-through of photovoltaic inverter; Indicates the correction slope value of the low voltage ride-through recovery process of the PV inverter; Indicates the reactive current command value when the low voltage ride-through of the photovoltaic inverter is at the lowest value;

[0017] in, , , , , , , , These are control parameters that affect the low voltage ride-through performance of new energy inverters.

[0018] Furthermore, in step S1, the sensitivity of the candidate parameters of the key control parameters to be optimized that affect the low voltage ride-through performance to the voltage fluctuation is analyzed, and based on the operating characteristics of the wind, solar, and thermal energy base, a fault set that causes the new energy inverter in the wind, solar, and thermal energy base to enter the low voltage ride-through state is constructed. It is expressed as:

[0019] ;

[0020] In the formula, For the A fault condition that causes the new energy inverter in the wind, solar and thermal energy base to enter the low voltage ride-through state; is the total number of faults;

[0021] Set a target key control parameter to be optimized and keep the selected parameter increasing from the minimum value of the value range to the maximum value of the target value by 20%. Scan all faults in the constructed fault set and take the maximum voltage fluctuation amplitude as the voltage sensitivity factor, which is expressed as:

[0022] ;

[0023] In the formula, For the The voltage sensitivity factor of the candidate parameter of the key control parameter to be optimized, For the The first candidate parameter of the key control parameter to be optimized Voltage fluctuation value under each fault scenario;

[0024] According to the above voltage sensitivity factor calculation method, the voltage sensitivity factor set of the candidate parameters of the key control parameters to be optimized is obtained. :

[0025] ;

[0026] For the Voltage sensitivity factors of candidate parameters of key control parameters to be optimized; is the voltage sensitivity factor of the candidate parameter of the Nth key control parameter to be optimized;

[0027] The voltage sensitivity factor set for the selected parameters The elements in are arranged in descending order, and the first 30% of the key control parameters to be optimized are selected and determined as the final key control parameters to be optimized.

[0028] Furthermore, in step S2, in order to determine the values ​​of the key control parameters to be optimized, a tuning model for optimizing the key control parameters of the low voltage ride-through of the new energy inverter is designed. The tuning model takes the minimum fluctuation range of the grid-connected voltage of the new energy station under typical working conditions as the objective function, and its expression is:

[0029] ;

[0030] In the formula, min Defined as the minimum value of the objective function; is the minimum value of the grid connection point voltage under typical working conditions, It is the rated voltage of the grid connection point of new energy stations in wind, solar and thermal energy bases.

[0031] Furthermore, in step S2, the actual operating range of the key control parameters to be optimized is taken as a constraint condition. In addition to satisfying the conventional stability constraints of frequency stability, voltage stability, and power angle stability in the power grid with a high proportion of new energy, the boundary range of the final key control parameter values ​​to be optimized is also considered. The upper and lower limits of the final key control parameters to be optimized are set as the upper and lower limits of the decision variables of the designed setting model. The expression is:

[0032] ;

[0033] In the formula, For the The final key control parameters to be optimized are determined; For the The final lower limit of the key control parameter to be optimized is determined; For the The final upper limit of the key control parameter to be optimized is determined.

[0034] Furthermore, in step S3, for the fault scenario, the particle swarm optimization algorithm is used to solve the tuning model, and the optimal key control parameters of the low voltage ride-through control performance of the new energy inverter are obtained in the following manner:

[0035] On the electromechanical transient simulation platform, the constructed fault set is scanned, the setting model is solved by the particle swarm optimization algorithm, and the maximum number of iterations of the particle swarm optimization algorithm is used as the convergence condition.

[0036] After each iteration, new key control parameters to be optimized are generated, which are applied to the control parameters of the target wind turbine in the electromechanical transient simulation platform. The voltage drop value of the new energy station under the current working condition is recorded and compared with the previous value to determine the parameter value for the next iteration. When the maximum number of iterations set by the particle swarm optimization algorithm converges, the key control parameters to be optimized at this time are the optimal key control parameters for low voltage ride-through of the new energy inverter.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows: based on the grid-related voltage control performance analysis formula of wind turbines and photovoltaic inverters, the present invention analyzes the control parameters that affect the low voltage ride-through performance and determines the final key control parameters to be optimized; taking the minimum grid-connected voltage fluctuation range of new energy stations as the objective function, combined with the actual operating range of the final key control parameters as constraints, the tuning model for optimizing the low voltage ride-through key control parameters of new energy inverters is designed, and the tuning model is solved by the particle swarm optimization algorithm to obtain the optimal key control parameters that can improve the low voltage ride-through control performance of new energy inverters. The inverter low voltage ride-through performance optimization method of the wind, light and thermal base based on parameter setting of the present invention sets the inverter control parameters, fully considers complex operating scenarios and various fault types, finds the optimal parameters that meet various operating conditions, promotes the low voltage ride-through level of new energy stations, and can make effective adjustments in time in the face of grid voltage fluctuations of new energy inverters, reduces the risk of disconnection of new energy stations due to grid failures, improves the voltage transient stability of new energy bases and the safety of the entire grid, and is one of the effective means to help large-scale grid connection of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flowchart of the steps of the inverter low-breakdown performance optimization method of the wind-solar-thermal base based on parameter setting proposed by the present invention;

[0039] Figure 2 This is a flow chart of solving the setting model using a particle swarm optimization algorithm for the inverter low-breakdown performance optimization method of a wind-solar-thermal base based on parameter setting proposed in the present invention. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0041] With the growing global demand for clean energy, new energy technologies such as wind power and photovoltaics are developing rapidly and becoming an important part of the power system. In particular, many wind, solar, and thermal energy bases have been formed in my country, and the proportion of new energy in these bases has increased significantly. However, with the large-scale grid connection of new energy, the stability of the power system is also facing new challenges. Due to the low proportion of traditional power sources in wind, solar, and thermal energy bases, it is impossible to effectively support the transient voltage stability level of new energy bases. It is urgent to tap the potential of new energy sites to actively support the power grid.

[0042] Therefore, in this embodiment, a method for optimizing the low-throughput performance of the inverter of the wind-solar-thermal base based on parameter setting is proposed. Figure 1 , the method comprises the following steps:

[0043] In step S1, based on the grid-related voltage control performance analysis formula of wind turbines and photovoltaic inverters, the control parameters that affect the low voltage ride-through performance are analyzed to determine the final key control parameters to be optimized. The specific operation process is as follows:

[0044] Analyze the grid-related voltage control performance of wind turbines and photovoltaic inverters, which is generally determined by the power output active current command in the low-voltage ride-through project of new energy inverters. and reactive current command It is determined that based on the grid-related voltage control performance analysis formula of wind turbines and photovoltaic inverters, the current control command expression of wind turbine inverter and the current control command expression of photovoltaic inverter are used for representation.

[0045] Among them, the current control instruction expression of the wind turbine inverter is:

[0046] ;

[0047] In the above formula, and They represent the active current command and reactive current command of the power output in the low voltage ride-through project of the new energy inverter. and is the active current calculation coefficient of low voltage ride through in the active current control instruction expression of the wind turbine inverter, The active current setting value for low voltage ride-through of the wind turbine inverter. and is the reactive current calculation coefficient of the low voltage ride through of the reactive current control instruction expression of the wind turbine inverter, Indicates the voltage value at which the wind turbine inverter enters low voltage ride-through. is the initial active current of the wind turbine inverter, and 0.9 is the voltage threshold for the wind turbine inverter to enter low voltage ride-through; is the initial reactive current of the wind turbine inverter; Set the reactive current value for low voltage ride-through of wind turbine inverter;

[0048] Among them, the current control instruction expression of the photovoltaic inverter is:

[0049] ;

[0050] In the formula, Indicates the active current command during the low voltage ride-through control process of the photovoltaic inverter; Indicates the reactive current command during the low voltage ride through control process of the photovoltaic inverter; Indicates the voltage of the photovoltaic cluster grid connection point detected during the low voltage drop process; Indicates the active power reference value of the PV cluster before the low voltage drops; Indicates the maximum current output by the photovoltaic inverter. It is the reactive current value of low voltage ride-through of photovoltaic inverter; Indicates the correction slope value of the low voltage ride-through recovery process of the PV inverter; Indicates the reactive current command value when the low voltage ride-through of the photovoltaic inverter is at the lowest value;

[0051] in, , , , , , , , All of them are control parameters that affect the low voltage ride-through performance of the new energy inverter, and can be considered as candidate parameters for the key control parameters to be optimized, thereby determining the candidate parameters for the key parameters that affect the low voltage ride-through performance of the new energy inverter.

[0052] The sensitivity of the candidate parameters of the key control parameters to be optimized that affect the low voltage ride-through performance to the voltage fluctuation is analyzed. Based on the operating characteristics of the wind, solar, and thermal energy base, a fault set that causes the new energy inverter in the wind, solar, and thermal energy base to enter the low voltage ride-through state is constructed. It is expressed as:

[0053] ;

[0054] In the formula, For the A fault condition that causes the new energy inverter in the wind, solar and thermal energy base to enter the low voltage ride-through state; is the total number of faults.

[0055] Set a target key control parameter to be optimized and keep the selected parameter increasing from the minimum value of the value range to the maximum value of the target value by 20%. Scan all faults in the constructed fault set and take the maximum voltage fluctuation amplitude as the voltage sensitivity factor, which is expressed as:

[0056] ;

[0057] In the formula, For the The voltage sensitivity factor of the candidate parameter of the key control parameter to be optimized, For the The first candidate parameter of the key control parameter to be optimized Voltage fluctuation value under each fault scenario;

[0058] According to the above voltage sensitivity factor calculation method, the voltage sensitivity factor set of the candidate parameters of the key control parameters to be optimized is obtained. :

[0059] ;

[0060] For the Voltage sensitivity factors of candidate parameters of key control parameters to be optimized; is the voltage sensitivity factor of the candidate parameter of the Nth key control parameter to be optimized; since there are 8 candidate parameters of the key control parameter to be optimized, N=8 here.

[0061] The voltage sensitivity factor set for the selected parameters The elements in are arranged in descending order, and the first 30% of the key control parameters to be optimized are selected and determined as the final key control parameters to be optimized.

[0062] In step S2, the minimum voltage fluctuation range of the new energy station grid connection is taken as the objective function, and the actual operating range of the key control parameters to be optimized is combined as the constraint condition to create a setting model for optimizing the key control parameters of the new energy inverter low voltage ride through. The operation process is as follows:

[0063] The tuning model for optimizing the key control parameters of low voltage ride-through of new energy inverters is designed with the minimum voltage fluctuation range of grid-connected new energy stations under typical working conditions as the objective function. In order to determine the values ​​of the key control parameters to be optimized, the tuning model for optimizing the key control parameters of low voltage ride-through of new energy inverters is designed. The tuning model takes the minimum voltage fluctuation range of grid-connected new energy stations under typical working conditions as the objective function, and its expression is:

[0064] ;

[0065] In the formula, min Defined as the minimum value of the objective function; is the lowest value of the grid connection point voltage under typical working conditions, It is the rated voltage of the grid connection point of new energy stations in wind, solar and thermal energy bases.

[0066] In terms of setting the constraints of the tuning model for optimizing the key control parameters of the designed new energy inverter low voltage ride through, the actual operating range of the key control parameters to be optimized is combined as the constraints. In addition to satisfying the conventional stability constraints of frequency stability, voltage stability, and power angle stability in the power grid with a high proportion of new energy, it is also necessary to consider the boundary range of the final key control parameter value to be optimized as the decision variable. The final value of the key control parameter to be optimized is taken as the upper and lower limits, and is set as the upper and lower limits of the decision variables of the designed tuning model as the constraints. The expression is:

[0067] ;

[0068] In the formula, For the The final key control parameters to be optimized are determined; For the The final lower limit of the key control parameter to be optimized is determined; For the The final upper limit of the key control parameter to be optimized is determined.

[0069] In step S3, for the fault scenario, the particle swarm optimization algorithm is used to solve the constructed tuning model to obtain the optimal key control parameters of the low voltage ride-through control performance of the new energy inverter. The method is as follows:

[0070] In order to solve the tuning model for optimizing the key control parameters of the designed new energy inverter low voltage ride through, on the electromechanical transient simulation platform, the fault set constructed is scanned for fault scenarios. These faults cause the new energy inverters in the wind, solar and thermal energy bases to enter the low voltage ride through state. The tuning model is solved by the particle swarm optimization algorithm, and the maximum number of iterations of the particle swarm optimization algorithm is used as the convergence condition. The particle swarm optimization algorithm solution process is as follows: Figure 2As shown in the figure, the algorithm first needs to initialize the particle swarm and set the algorithm parameters; then calculate the fitness of the particles. After obtaining the individual historical optimal position pbest and the group optimal position gbest, according to the speed and state calculation method defined, the state is updated according to the formula to determine whether the termination condition is met. If not, it returns to recalculate the fitness of the particles. If the termination condition is met, the solution process ends and the result is output.

[0071] In this embodiment, the tuning model is solved by the particle swarm optimization algorithm. During the optimization process, new key control parameters to be optimized are generated after each iteration, which are applied to the control parameters of the target wind turbine in the electromechanical transient simulation platform. The voltage drop value of the new energy station under the current working condition is recorded and compared with the previous value to determine the parameter value for the next iteration. When the maximum number of iterations set by the particle swarm optimization algorithm converges, the key control parameters to be optimized at this time are the optimal key control parameters for low voltage ride-through of the new energy inverter.

[0072] In this embodiment, firstly, based on the grid-related voltage control performance analysis formula of wind turbines and photovoltaic inverters, the control parameters affecting the low voltage ride-through performance are analyzed to determine the final key control parameters to be optimized; secondly, taking the minimum grid-connected voltage fluctuation range of new energy stations as the objective function, combined with the actual operating range of the final key control parameters as constraints, a tuning model for optimizing the key control parameters of the low voltage ride-through of new energy inverters is designed; finally, for the constructed fault scenario, the particle swarm optimization algorithm is used to solve the tuning model to obtain the optimal key control parameters that can improve the low voltage ride-through control performance of the new energy inverter.

[0073] The inverter low-voltage ride-through performance optimization method for wind, solar and thermal bases based on parameter setting proposed in this embodiment, by setting the inverter control parameters, fully considers complex operating scenarios and various fault types, finds the optimal parameters that meet various operating conditions, and promotes the low-voltage ride-through level of new energy sites. In the face of grid voltage fluctuations, new energy inverters can make effective adjustments in time, reducing the risk of new energy sites being disconnected from the grid due to grid failures, improving the voltage transient stability of new energy bases and the safety of the entire grid, and is one of the effective means to assist large-scale grid connection of new energy.

[0074] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. The inverter low-power performance optimization method of wind, solar and thermal base based on parameter setting is characterized by: The following steps are involved: Step S1: Based on the grid-related voltage control performance analysis formula of wind turbines and photovoltaic inverters, analyze the control parameters that affect the low voltage ride-through performance and determine the final key control parameters to be optimized; Step S2: Taking the minimum voltage fluctuation range of the new energy station as the objective function and combining the actual operating range of the key control parameters to be optimized as the constraint condition, a setting model for optimizing the key control parameters of the new energy inverter low voltage ride-through is created; In step S3, for the fault scenario, the particle swarm optimization algorithm is used to solve the tuning model and obtain the optimal key control parameters for the low voltage ride-through control performance of the new energy inverter.

2. The method for optimizing the low-power-breakdown performance of wind-solar-thermal base inverters based on parameter setting according to claim 1 is characterized in that: In step S1, the grid-related voltage control performance of wind turbines and photovoltaic inverters is determined by the active current command and reactive current command in the low-voltage ride-through project of the new energy inverter. Based on the grid-related voltage control performance analysis formula of wind turbines and photovoltaic inverters, the current control command expression of the wind turbine inverter and the current control command expression of the photovoltaic inverter are used to express them; The current control command expression of the wind turbine inverter is: ； In the above formula, and They represent the active current command and reactive current command of the power output in the low voltage ride-through project of the new energy inverter. and is the active current calculation coefficient of low voltage ride through in the active current control instruction expression of the wind turbine inverter, The active current setting value for low voltage ride-through of the wind turbine inverter. and is the reactive current calculation coefficient of the low voltage ride through of the reactive current control instruction expression of the wind turbine inverter, Indicates the voltage value at which the wind turbine inverter enters low voltage ride-through. is the initial active current of the wind turbine inverter, and 0.9 is the voltage threshold for the wind turbine inverter to enter low voltage ride-through; is the initial reactive current of the wind turbine inverter; Set the reactive current value for low voltage ride-through of wind turbine inverter; The current control command expression of the photovoltaic inverter is: ； In the formula, Indicates the active current command during the low voltage ride-through control process of the photovoltaic inverter; Indicates the reactive current command during the low voltage ride through control process of the photovoltaic inverter; Indicates the voltage of the photovoltaic cluster grid connection point detected during the low voltage drop process; Indicates the active power reference value of the PV cluster before the low voltage drops; Indicates the maximum current output by the photovoltaic inverter. It is the reactive current value of low voltage ride-through of photovoltaic inverter; Indicates the correction slope value of the low voltage ride-through recovery process of the PV inverter; Indicates the reactive current command value when the low voltage ride-through of the photovoltaic inverter is at the lowest value; in, , , , , , , , These are control parameters that affect the low voltage ride-through performance of new energy inverters.

3. The method for optimizing the low-breakdown performance of the inverter of the wind-solar-thermal base based on parameter setting according to claim 1 is characterized in that: In step S1, the sensitivity of the candidate parameters of the key control parameters to be optimized that affect the low voltage ride-through performance to the voltage fluctuation is analyzed. Based on the operating characteristics of the wind, solar, and thermal energy base, a fault set that causes the new energy inverter in the wind, solar, and thermal energy base to enter the low voltage ride-through state is constructed. It is expressed as: ； In the formula, For the A fault condition that causes the new energy inverter in the wind, solar and thermal energy base to enter the low voltage ride-through state; is the total number of faults; Set a target key control parameter to be optimized and keep the selected parameter increasing from the minimum value of the value range to the maximum value of the target value by 20%. Scan all faults in the constructed fault set and take the maximum voltage fluctuation amplitude as the voltage sensitivity factor, which is expressed as: ； In the formula, For the The voltage sensitivity factor of the candidate parameter of the key control parameter to be optimized, For the The first candidate parameter of the key control parameter to be optimized Voltage fluctuation value under each fault scenario; According to the above voltage sensitivity factor calculation method, the voltage sensitivity factor set of the candidate parameters of the key control parameters to be optimized is obtained. : ； For the Voltage sensitivity factors of candidate parameters of key control parameters to be optimized; is the voltage sensitivity factor of the candidate parameter of the Nth key control parameter to be optimized; The voltage sensitivity factor set for the selected parameters The elements in are arranged in descending order, and the first 30% of the key control parameters to be optimized are selected and determined as the final key control parameters to be optimized.

4. The method for optimizing the low-breakdown performance of wind-solar-thermal base inverters based on parameter setting according to claim 1 is characterized in that: In step S2, in order to determine the values ​​of the key control parameters to be optimized, a tuning model for optimizing the key control parameters of the low voltage ride-through of the new energy inverter is designed. The tuning model takes the minimum voltage fluctuation range of the new energy station grid under typical working conditions as the objective function, and its expression is: ； In the formula, min is defined as the minimum value of the objective function, is the minimum value of the grid connection point voltage under typical working conditions, It is the rated voltage of the grid connection point of new energy stations in wind, solar and thermal energy bases.

5. The inverter low-breakdown performance optimization method for wind, solar and thermal power bases based on parameter setting as described in claim 1 is characterized in that: In step S2, the actual operating range of the key control parameter to be optimized is taken as a constraint condition, and the upper and lower limits of the values ​​of the key control parameter to be optimized are set as the upper and lower limits of the designed setting model decision variables, and the expression is: ； In the formula, For the The final key control parameters to be optimized are determined; For the The final lower limit of the key control parameter to be optimized is determined; For the The final upper limit of the key control parameter to be optimized is determined.

6. The method for optimizing the low-breakdown performance of wind-solar-thermal base inverters based on parameter setting according to claim 1 is characterized in that: In step S3, for the fault scenario, the particle swarm optimization algorithm is used to solve the tuning model and obtain the optimal key control parameters of the low voltage ride-through control performance of the new energy inverter in the following way: On the electromechanical transient simulation platform, the constructed fault set is scanned, the setting model is solved by the particle swarm optimization algorithm, and the maximum number of iterations of the particle swarm optimization algorithm is used as the convergence condition. After each iteration, new key control parameters to be optimized are generated, which are applied to the control parameters of the target wind turbine in the electromechanical transient simulation platform. The voltage drop value of the new energy station under the current working condition is recorded and compared with the previous value to determine the parameter value for the next iteration. When the maximum number of iterations set by the particle swarm optimization algorithm converges, the key control parameters to be optimized at this time are the optimal key control parameters for low voltage ride-through of the new energy inverter.

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