Two-stage optimization method and system for configuring wind power sending end grid compensator

Abstract

The application discloses a wind power sending end power grid compensator two-stage optimization configuration method and system, relates to the technical field of optimization configuration, and comprises the following steps: S1, constructing a simulation model, S2, determining a target function, S3, outputting a scheme set and S4, screening a configuration scheme.The application is provided with a progressive architecture of steady-state screening-dynamic checking, preliminary optimization is carried out in the first stage, it is ensured that the candidate scheme has good static voltage support capability, and the candidate scheme has close electrical connection with key dynamic elements such as a wind power cluster and a direct current system, a foundation is laid for coordinated control in a dynamic process, and the second stage quantitatively evaluates and sorts the dynamic interaction behavior of the candidate scheme through strict time domain simulation and eigenvalue analysis, so that the configuration scheme that may cause system oscillation or instability is actively avoided in the final decision.

Description

Technical Field

[0001] This invention relates to the field of optimization configuration technology, specifically to a two-stage optimization configuration method and system for synchronous condensers at the wind power transmission end of the power grid. Background Technology

[0002] A two-stage optimization method for synchronous condenser configuration in wind power transmission grids combines steady-state analysis with dynamic interactive evaluation to achieve synergistic optimization of synchronous condenser configuration between static performance and dynamic stability, effectively avoiding unfavorable interactions between control systems. Patent application number 202411462361.8 discloses "an optimization algorithm for the capacity configuration of synchronous condensers in offshore wind farms, the optimization process being divided into two stages: the first stage aims to improve the short-circuit ratio of the offshore wind farm, and the second stage aims to suppress overvoltage. This invention first establishes the objective functions and constraints for the two stages. Then, the Flying Eagle swarm intelligence optimization algorithm is used to solve the objective functions to obtain the capacity of the synchronous condensers that should be configured in each sub-wind farm of the offshore wind farm."

[0003] The aforementioned existing technologies have solved problems such as the inability to reasonably allocate the capacity of synchronous condensers. However, during system operation, the existing configuration methods do not fully consider the impact of dynamic interactions. The planned installation location and capacity of the synchronous condensers may be optimal under static conditions, but may have a negative effect during dynamic processes, reducing the actual level of safety and stability. Summary of the Invention

[0004] The purpose of this invention is to provide a two-stage optimization configuration method and system for synchronous condensers at the wind power transmission end of the power grid, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a two-stage optimization configuration method for synchronous condensers at the wind power transmission end of the power grid, comprising the following steps: S1. Constructing a simulation model: Establishing a detailed model of the wind power transmission grid, which includes a three-phase power flow calculation basic model, mathematical models of grid-side converters and machine-side converters, a sixth-order practical model of the synchronous condenser body, and a topology model. S2. Determine the objective function: Define the steady-state optimization objective and constraints. Construct the objective function with the goal of minimizing the overall network loss, minimizing the voltage deviation, and maximizing the static voltage stability margin. Set equality constraints and inequality constraints. S3. Output Scheme Set: Genetic algorithm is used to optimize and generate a set of candidate camera configuration schemes with good steady-state performance; S4. Selecting Configuration Schemes: Construct a dynamic interactive evaluation index system, and select the scheme with the best comprehensive ranking and that meets the preset dynamic security threshold as the final configuration scheme.

[0006] Preferably, step S1 specifically includes the following steps: S101. Collect power grid topology, line parameters, transformer parameters and load data, determine the geographical location and electrical connection relationship of wind farm clusters, DC converter stations, reactive power compensation devices and candidate synchronous condenser nodes, and construct a three-phase power flow calculation basic model including AC aggregation network and backbone network. S102. Read the equipment factory test report and grid connection test data, and establish mathematical models of grid-side converter and machine-side converter for wind turbines corresponding to doubly-fed induction generators and permanent magnet synchronous generators. The model includes a power outer loop controller, a current inner loop controller, a phase-locked loop system and a DC bus voltage controller, and sets the proportional-integral adjustment parameters and the time constant of the filter link. S103. Establish a sixth-order practical model of the synchronous condenser body, including the excitation system and the automatic voltage regulator. The automatic voltage regulator model includes a measurement unit, an excitation limiter, and a power system stabilizer module. For the static var generator and static var compensator, construct their topology model based on fully controlled devices and thyristors, and integrate their outer loop voltage control and inner loop current control loops.

[0007] Preferably, step S1 further includes the following steps: S104. For DC transmission systems, establish equivalent circuits for converter transformers, twelve-pulse converter bridges, and AC filters, and configure control system hierarchies for rectifier and inverter sides, including switching logic for constant power control and constant extinction angle control, voltage-dependent current limiting modules, and additional damping controllers. S105. Connect all component models electrically in the MATLAB simulation platform, set a unified base value system, perform power flow calculations, adjust the generator output, transformer tap and reactive power compensation device settings to make them run under the preset initial conditions, verify the consistency between the initialization results of all dynamic model state variables and the power flow solution, and complete the construction of the electromechanical-electromagnetic hybrid simulation platform.

[0008] Preferably, step S2 specifically includes the following steps: S201. A multi-objective optimization function is constructed to minimize overall network loss, minimize node voltage deviation, and maximize static voltage stability margin. The multi-objective optimization function is as follows: , , , , in, This represents the objective function for minimizing network loss. Represents nodes on the branch voltage amplitude, Represents nodes on the branch voltage amplitude, Indicates the connection node With nodes The voltage phase angle difference between the two ends of the branch, Indicates branch conductance, Represents the set of all branches. Indicates the branch number. This represents the objective function for minimizing node voltage deviation. Represents a node Rated voltage, Represents the set of all nodes. Indicates the node index. This represents the objective function that minimizes the negative value of the static voltage stability margin. This represents a parameter indicating load growth capacity. Represents a multi-objective optimization function; S202. Set equality constraints and inequality constraints. The equality constraints are the node power balance equations, which stipulate that the active power injected into each node and the reactive power must be equal to the power flowing out of the network. The node power balance equations are as follows: , , in, Indicates at node The total active power generated by all generators. Indicates at node The total active power consumed by all loads. Represents a node voltage amplitude, Represents nodes Adjacent nodes voltage amplitude, Represents a node and nodes The voltage phase angle difference between them Indicates electrical conductance. Indicates susceptance. Indicates the node index. Indicates at node The total reactive power generated by all generators Indicates at node The total reactive power generated by all the cameras. Indicates at node The total reactive power consumed by all loads. Represents nodes The number of directly connected nodes; S203. The inequality constraints include that the voltage amplitude of all nodes must be maintained between the preset safety upper and lower limits, the apparent power flow of all transmission lines must not exceed their thermal stability limits, the reactive power output of each synchronous condenser is limited between the minimum and maximum capacity of its equipment, and the active power output of the wind farm is constrained between zero and the current predicted output value.

[0009] Preferably, step S3 specifically includes the following steps: S301. Randomly generate a set of initial candidate solutions to form the initial population. ,in , Indicates the first Individual, Indicates the first Individual, Indicates the camera adjustment number Each position value Indicates the camera adjustment number One capacity value, This represents the total number of individuals. This indicates the total number of camera tuners, with each individual code representing the camera tuner's configuration scheme. S302. In each iteration, for each individual in the population The system verifies whether it satisfies equality and inequality constraints through power flow calculations. For individuals that do not meet the constraints, a penalty function method is used to degrade their objective function value. The specific objective function value is as follows: , in, Represents an individual The degraded objective function value Represents an individual The objective function value, Indicates the degree of constraint violation. Indicates the penalty coefficient; S303. Calculate the objective function vector of all individuals that satisfy the constraints and have been processed by the penalty function, and use the fast non-dominated sorting algorithm to divide the individuals in the population into multiple non-dominated solution sets.

[0010] Preferably, step S3 further includes the following steps: S304. Starting from the highest level non-dominated solution set, individuals are selected into the next generation population layer by layer until the population size exceeds the preset limit due to the addition of solution sets. When the preset limit is exceeded, the crowding degree of individuals in the solution set is calculated. Individuals in the over-limit solution set are sorted from largest to smallest crowding degree. The individual with the highest crowding degree is selected to fill the remaining population slots, ensuring that solutions located in sparse regions of the target space are reserved first. S305. After the selection is completed, a new population is generated through genetic operators. Simulated binary crossover and polynomial mutation operations are applied to the selected individuals. The newly generated offspring population is merged with the selected parent population to form a new population. S306. Each individual updates its historical optimal solution, while the global optimal solution is determined from the current non-dominated solution set based on the maximum crowding. This process is repeated until the current iteration count is greater than or equal to the maximum iteration count, and the last non-dominated solution set that currently represents the optimal solution set is output.

[0011] Preferably, step S4 specifically includes the following steps: S401. Construct a dynamic interactive evaluation index system, wherein the indexes include damping ratio index, voltage overshoot and recovery time index, and interactive energy index; S402. After reading all individuals in the non-dominated solution set, generate corresponding candidate configuration schemes according to the individuals, apply a standard large perturbation sequence to each scheme, obtain the transient response trajectory through time-domain simulation, simultaneously perform small perturbation eigenvalue analysis to extract the system oscillation mode, and quantify and calculate various dynamic interaction indicators based on simulation data. S403. Determine the objective weights of each indicator using the entropy weight method, establish a weighted normalized decision matrix, use the TOPSIS method to calculate the relative closeness of each scheme to the ideal solution, generate a comprehensive ranking result, and select the scheme with the best comprehensive ranking and that meets the preset dynamic safety threshold as the final configuration scheme.

[0012] The two-stage optimization configuration system for the wind power transmission-end grid synchronous condenser includes a model building unit, a constraint generation unit, a scheme generation unit, and a scheme screening unit. The model building unit establishes a detailed model of the wind power transmission grid, which includes a three-phase power flow calculation basic model, mathematical models of grid-side converters and machine-side converters, a sixth-order practical model of the synchronous condenser body, and a topology model. The constraint generation unit defines steady-state optimization objectives and constraints, constructs an objective function with the goal of minimizing overall system network loss, minimizing voltage deviation, and maximizing static voltage stability margin, and sets equality constraints and inequality constraints. The scheme generation unit uses a genetic algorithm to optimize and generate a set of candidate camera configuration schemes with good steady-state performance. The scheme selection unit constructs a dynamic interactive evaluation index system, and selects the scheme with the best comprehensive ranking and that meets the preset dynamic security threshold as the final configuration scheme according to the index.

[0013] Compared with the prior art, the beneficial effects of the present invention are: This invention employs a progressive architecture of steady-state screening and dynamic verification. In the first stage, preliminary optimization is performed to ensure that candidate schemes not only have good static voltage support capabilities but also have close electrical connections with key dynamic components such as wind power clusters and DC systems, laying the foundation for coordinated control during dynamic processes. In the second stage, rigorous time-domain simulation and eigenvalue analysis are used to quantitatively evaluate and rank the dynamic interaction behavior of candidate schemes, thereby proactively avoiding configuration schemes that may cause system oscillations or instability in the final decision. Attached Figure Description

[0014] Figure 1 An overall method flowchart is provided for embodiments of the present invention. Detailed Implementation

[0015] 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, and 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.

[0016] Example 1: Please refer to Figure 1 This invention provides a technical solution: a two-stage optimized configuration method for synchronous condensers at the wind power transmission end of the power grid, comprising the following steps: S1. Constructing a simulation model: Establish a detailed model of the wind power transmission grid, including a basic model for three-phase power flow calculation, mathematical models of grid-side converters and generator-side converters, a sixth-order practical model of the synchronous condenser body, and a topology model. S2. Determine the objective function: Define the steady-state optimization objective and constraints. Construct the objective function with the goal of minimizing the overall network loss, minimizing the voltage deviation, and maximizing the static voltage stability margin. Set equality constraints and inequality constraints. S3. Output Scheme Set: Genetic algorithm is used to optimize and generate a set of candidate camera configuration schemes with good steady-state performance; S4. Selecting Configuration Schemes: Construct a dynamic interactive evaluation index system, and select the scheme with the best comprehensive ranking and that meets the preset dynamic security threshold as the final configuration scheme.

[0017] S1 specifically includes the following steps: S101. Collect power grid topology, line parameters, transformer parameters and load data, determine the geographical location and electrical connection relationship of wind farm clusters, DC converter stations, reactive power compensation devices and candidate synchronous condenser nodes, and construct a three-phase power flow calculation basic model including AC aggregation network and backbone network. S102. Read the equipment factory test report and grid connection test data, and establish mathematical models of grid-side converter and machine-side converter for wind turbines corresponding to doubly-fed induction generators and permanent magnet synchronous generators. The model includes a power outer loop controller, a current inner loop controller, a phase-locked loop system and a DC bus voltage controller, and sets the proportional-integral adjustment parameters and the time constant of the filter link. S103. Establish a sixth-order practical model of the synchronous condenser body, including the excitation system and the automatic voltage regulator. The automatic voltage regulator model includes a measurement unit, an excitation limiter and a power system stabilizer module. For the static var generator and the static var compensator, construct their topology model based on fully controlled devices and thyristors, and integrate their outer loop voltage control and inner loop current control loops. S1 further includes the following steps: S104. For DC transmission systems, establish equivalent circuits for converter transformers, twelve-pulse converter bridges, and AC filters, and configure control system hierarchies for rectifier and inverter sides, including switching logic for constant power control and constant extinction angle control, voltage-dependent current limiting modules, and additional damping controllers. S105. Connect all component models electrically in the MATLAB simulation platform, set a unified base value system, perform power flow calculations, adjust the generator output, transformer tap and reactive power compensation device settings to make them run under the preset initial conditions, verify the consistency between the initialization results of all dynamic model state variables and the power flow solution, and complete the construction of the electromechanical-electromagnetic hybrid simulation platform. S2 specifically includes the following steps: S201. A multi-objective optimization function is constructed to minimize overall network loss, minimize node voltage deviation, and maximize static voltage stability margin. The multi-objective optimization function is as follows: , , , , in, This represents the objective function for minimizing network loss. Represents nodes on the branch voltage amplitude, Represents nodes on the branch voltage amplitude, Indicates the connection node With nodes The voltage phase angle difference between the two ends of the branch, Indicates branch conductance, Represents the set of all branches. Indicates the branch number. This represents the objective function for minimizing node voltage deviation. Represents a node Rated voltage, Represents the set of all nodes. Indicates the node index. This represents the objective function that minimizes the negative value of the static voltage stability margin. This represents a parameter indicating load growth capacity. Represents a multi-objective optimization function; S202. Set equality constraints and inequality constraints. The equality constraints are the node power balance equations, which stipulate that the active power injected into each node and the reactive power must be equal to the power flowing out of the network. The specific node power balance equations are as follows: , , in, Indicates at node The total active power generated by all generators. Indicates at node The total active power consumed by all loads. Represents a node voltage amplitude, Represents nodes Adjacent nodes voltage amplitude, Represents a node and nodes The voltage phase angle difference between them Indicates electrical conductance. Indicates susceptance. Indicates the node index. Indicates at node The total reactive power generated by all generators Indicates at node The total reactive power generated by all the cameras. Indicates at node The total reactive power consumed by all loads. Represents nodes The number of directly connected nodes; S203. Inequality constraints include: all node voltage amplitudes must be maintained between preset safety upper and lower limits; the apparent power flow of all transmission lines must not exceed their thermal stability limits; the reactive power output of each synchronous condenser is limited between its minimum and maximum capacity; and the active power output of the wind farm is constrained between zero and the current predicted output value. Specifically, the inequality constraints are as follows: , , , , in, Represents a node The lower limit of the allowable voltage, Represents a node The upper limit of the allowable voltage, Represents a node The actual operating voltage amplitude, Indicates flow through a branch Apparent power Indicates a branch Maximum allowed transmission capacity Represents the set of all branches in the network. Represents the set of all nodes in the network. This indicates the lower limit of the reactive power output of the camera. This indicates the upper limit of the reactive power output of the camera. This indicates the actual reactive power generated by the synchronous condenser. This represents the total active power actually generated by the wind farm. This indicates the predicted active power output of the wind farm. Indicates the node index. Indicates the branch number; S3 specifically includes the following steps: S301. Randomly generate a set of initial candidate solutions to form the initial population. ,in , Indicates the first Individual, Indicates the first Individual, Indicates the camera adjustment number Each position value Indicates the camera adjustment number One capacity value, This represents the total number of individuals. This indicates the total number of camera tuners, with each individual code representing the camera tuner's configuration scheme. S302. In each iteration, for each individual in the population The equality and inequality constraints are verified through power flow calculations. For individuals that do not meet the constraints, the objective function value is degraded using a penalty function method. The specific objective function value is as follows: , in, Represents an individual The degraded objective function value Represents an individual The objective function value, Indicates the degree of constraint violation. Indicates the penalty coefficient; S303. Calculate the objective function vector of all individuals that satisfy the constraints and are processed by the penalty function. Use the fast non-dominated sorting algorithm to divide the individuals in the population into multiple non-dominated solution sets. S3 also includes the following steps: S304. Starting from the highest level non-dominated solution set, individuals are selected into the next generation population layer by layer until the population size exceeds the preset limit due to the addition of solution sets. When the preset limit is exceeded, the crowding degree of individuals in the solution set is calculated. Individuals in the over-limit solution set are sorted from largest to smallest crowding degree. The individual with the highest crowding degree is selected to fill the remaining population slots, ensuring that solutions located in sparse regions of the target space are reserved first. S305. After the selection is completed, a new population is generated through genetic operators. Simulated binary crossover and polynomial mutation operations are applied to the selected individuals. The newly generated offspring population is merged with the selected parent population to form a new population. S306. Each individual updates its historical optimal solution, while the global optimal solution is determined from the current non-dominated solution set based on the maximum crowding. This process is repeated until the current iteration count is greater than or equal to the maximum iteration count, and the last non-dominated solution set that currently represents the optimal solution set is output. S4 specifically includes the following steps: S401. Construct a dynamic interactive evaluation index system, including damping ratio index, voltage overshoot and recovery time index, and interactive energy index. S402. After reading all individuals in the non-dominated solution set, generate corresponding candidate configuration schemes according to the individuals, apply a standard large perturbation sequence to each scheme, obtain the transient response trajectory through time-domain simulation, simultaneously perform small perturbation eigenvalue analysis to extract the system oscillation mode, and quantify and calculate various dynamic interaction indicators based on simulation data. S403. Determine the objective weights of each indicator using the entropy weight method, establish a weighted normalized decision matrix, use the TOPSIS method to calculate the relative closeness of each scheme to the ideal solution, generate a comprehensive ranking result, and select the scheme with the best comprehensive ranking and that meets the preset dynamic safety threshold as the final configuration scheme.

[0018] Example 2: The present invention also provides a two-stage optimization configuration system for wind power transmission-end grid synchronous condensers, including a model building unit, a constraint generation unit, a scheme generation unit, and a scheme screening unit; The model building unit establishes a detailed model of the wind power transmission grid, which includes a three-phase power flow calculation basic model, mathematical models of grid-side converters and machine-side converters, a sixth-order practical model of the synchronous condenser body, and a topology model. The constraint generation unit defines the steady-state optimization objective and constraints, constructs the objective function with the goal of minimizing the overall network loss, minimizing the voltage deviation, and maximizing the static voltage stability margin, and sets equality constraints and inequality constraints. The scheme generation unit uses a genetic algorithm to optimize and generate a set of candidate camera configuration schemes with good steady-state performance. The scheme selection unit constructs a dynamic interactive evaluation index system, and selects the scheme with the best comprehensive ranking and that meets the preset dynamic security threshold as the final configuration scheme.

[0019] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0020] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A two-stage optimization configuration method for synchronous condensers in the wind power transmission grid, characterized in that, The method includes the following steps: S1. Constructing a simulation model: Establishing a detailed model of the wind power transmission grid, which includes a three-phase power flow calculation basic model, mathematical models of grid-side converters and machine-side converters, a sixth-order practical model of the synchronous condenser body, and a topology model. S2. Determine the objective function: Define the steady-state optimization objective and constraints. Construct the objective function with the goal of minimizing the overall network loss, minimizing the voltage deviation, and maximizing the static voltage stability margin. Set equality constraints and inequality constraints. S3. Output Scheme Set: Genetic algorithm is used to optimize and generate a set of candidate camera configuration schemes with good steady-state performance; S4. Selecting Configuration Schemes: Construct a dynamic interactive evaluation index system, and select the scheme with the best comprehensive ranking and that meets the preset dynamic security threshold as the final configuration scheme.

2. The two-stage optimized configuration method for synchronous condensers at the wind power transmission end of the power grid according to claim 1, characterized in that: S1 specifically includes the following steps: S101. Collect power grid topology, line parameters, transformer parameters and load data, determine the geographical location and electrical connection relationship of wind farm clusters, DC converter stations, reactive power compensation devices and candidate synchronous condenser nodes, and construct a three-phase power flow calculation basic model including AC aggregation network and backbone network. S102. Read the equipment factory test report and grid connection test data, and establish mathematical models of grid-side converter and machine-side converter for wind turbines corresponding to doubly-fed induction generators and permanent magnet synchronous generators. The model includes a power outer loop controller, a current inner loop controller, a phase-locked loop system and a DC bus voltage controller, and sets the proportional-integral adjustment parameters and the time constant of the filter link. S103. Establish a sixth-order practical model of the synchronous condenser body, including the excitation system and the automatic voltage regulator. The automatic voltage regulator model includes a measurement unit, an excitation limiter, and a power system stabilizer module. For the static var generator and static var compensator, construct their topology model based on fully controlled devices and thyristors, and integrate their outer loop voltage control and inner loop current control loops.

3. The two-stage optimized configuration method for synchronous condensers at the wind power transmission end of the power grid according to claim 2, characterized in that: S1 further includes the following steps: S104. For DC transmission systems, establish equivalent circuits for converter transformers, twelve-pulse converter bridges, and AC filters, and configure control system hierarchies for rectifier and inverter sides, including switching logic for constant power control and constant extinction angle control, voltage-dependent current limiting modules, and additional damping controllers. S105. Connect all component models electrically in the MATLAB simulation platform, set a unified base value system, perform power flow calculations, adjust the generator output, transformer tap and reactive power compensation device settings to make them run under the preset initial conditions, verify the consistency between the initialization results of all dynamic model state variables and the power flow solution, and complete the construction of the electromechanical-electromagnetic hybrid simulation platform.

4. The two-stage optimized configuration method for synchronous condensers at the wind power transmission end of the power grid according to claim 1, characterized in that: S2 specifically includes the following steps: S201. A multi-objective optimization function is constructed to minimize overall network loss, minimize node voltage deviation, and maximize static voltage stability margin. S202. Set equality constraints and inequality constraints. The equality constraints are the node power balance equations, which stipulate that the active power injected into each node and the reactive power must be equal to the power flowing out of the network. The node power balance equations are as follows: in, Indicates at node The total active power generated by all generators. Indicates at node The total active power consumed by all loads. Represents a node voltage amplitude, Represents nodes Adjacent nodes voltage amplitude, Represents a node and nodes The voltage phase angle difference between them Indicates electrical conductance. Indicates susceptance. Indicates the node index. Indicates at node The total reactive power generated by all generators Indicates at node The total reactive power generated by all the cameras. Indicates at node The total reactive power consumed by all loads. Represents nodes The number of directly connected nodes; S203. The inequality constraints include that the voltage amplitude of all nodes must be maintained between the preset safety upper and lower limits, the apparent power flow of all transmission lines must not exceed their thermal stability limits, the reactive power output of each synchronous condenser is limited between the minimum and maximum capacity of its equipment, and the active power output of the wind farm is constrained between zero and the current predicted output value.

5. The two-stage optimized configuration method for synchronous condensers at the wind power transmission end of the power grid according to claim 1, characterized in that: S3 specifically includes the following steps: S301. Randomly generate a set of initial candidate solutions to form the initial population. ,in , Indicates the first Individual, Indicates the first Individual, Indicates the camera adjustment number Each position value Indicates the camera adjustment number One capacity value, This represents the total number of individuals. This indicates the total number of camera tuners, with each individual code representing the camera tuner's configuration scheme. S302. In each iteration, for each individual in the population The system verifies whether it satisfies equality and inequality constraints through power flow calculations. For individuals that do not meet the constraints, a penalty function method is used to degrade their objective function value. The specific objective function value is as follows: in, Represents an individual The degraded objective function value Represents an individual The objective function value, Indicates the degree of constraint violation. Indicates the penalty coefficient; S303. Calculate the objective function vector of all individuals that satisfy the constraints and have been processed by the penalty function, and use the fast non-dominated sorting algorithm to divide the individuals in the population into multiple non-dominated solution sets.

6. The two-stage optimized configuration method for synchronous condensers at the wind power transmission end of the power grid according to claim 5, characterized in that: S3 further includes the following steps: S304. Starting from the highest level non-dominated solution set, individuals are selected into the next generation population layer by layer until the population size exceeds the preset limit due to the addition of solution sets. When the preset limit is exceeded, the crowding degree of individuals in the solution set is calculated. Individuals in the over-limit solution set are sorted from largest to smallest crowding degree. The individual with the highest crowding degree is selected to fill the remaining population slots, ensuring that solutions located in sparse regions of the target space are reserved first. S305. After the selection is completed, a new population is generated through genetic operators. Simulated binary crossover and polynomial mutation operations are applied to the selected individuals. The newly generated offspring population is merged with the selected parent population to form a new population. S306. Each individual updates its historical optimal solution, while the global optimal solution is determined from the current non-dominated solution set based on the maximum crowding. This process is repeated until the current iteration count is greater than or equal to the maximum iteration count, and the last non-dominated solution set that currently represents the optimal solution set is output.

7. The two-stage optimized configuration method for synchronous condensers at the wind power transmission end of the power grid according to claim 1, characterized in that: S4 specifically includes the following steps: S401. Construct a dynamic interactive evaluation index system, wherein the indexes include damping ratio index, voltage overshoot and recovery time index, and interactive energy index; S402. After reading all individuals in the non-dominated solution set, generate corresponding candidate configuration schemes according to the individuals, apply a standard large perturbation sequence to each scheme, obtain the transient response trajectory through time-domain simulation, simultaneously perform small perturbation eigenvalue analysis to extract the system oscillation mode, and quantify and calculate various dynamic interaction indicators based on simulation data. S403. Determine the objective weights of each indicator using the entropy weight method, establish a weighted normalized decision matrix, use the TOPSIS method to calculate the relative closeness of each scheme to the ideal solution, generate a comprehensive ranking result, and select the scheme with the best comprehensive ranking and that meets the preset dynamic safety threshold as the final configuration scheme.

8. A two-stage optimized configuration system for synchronous condensers at the wind power transmission end of the power grid, characterized in that, The two-stage optimization configuration system for synchronous condensers is applicable to the two-stage optimization configuration method for synchronous condensers in the wind power transmission grid as described in any one of claims 1-7, and includes a model building unit, a constraint generation unit, a scheme generation unit, and a scheme screening unit; The model building unit establishes a detailed model of the wind power transmission grid, which includes a three-phase power flow calculation basic model, mathematical models of grid-side converters and machine-side converters, a sixth-order practical model of the synchronous condenser body, and a topology model. The constraint generation unit defines steady-state optimization objectives and constraints, constructs an objective function with the goal of minimizing overall system network loss, minimizing voltage deviation, and maximizing static voltage stability margin, and sets equality constraints and inequality constraints. The scheme generation unit uses a genetic algorithm to optimize and generate a set of candidate camera configuration schemes with good steady-state performance. The scheme selection unit constructs a dynamic interactive evaluation index system, and selects the scheme with the best comprehensive ranking and that meets the preset dynamic security threshold as the final configuration scheme according to the index.

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