Doubly-fed fan electromechanical model low-crossing fault impedance identification system and method based on dichotomy

Abstract

The system comprises an identification module and a comparison module, the identification module is used for new energy controller electromechanical model parameter identification, the parameter identification is based on hardware-in-the-loop measured data, and the comparison module is used for comparing the hardware-in-the-loop measured data with the hardware-in-the-loop measured data. The value of the drop impedance in the identification model is automatically modified by writing a binary search optimization algorithm until the drop voltage identification precision requirement is met, and an optimal identification result is automatically output; the comparison module is used for performing new energy electromechanical transient modeling test, verifying the correctness of a drop impedance identification result in the identification module, automatically performing electromechanical transient simulation by setting different test working conditions, and automatically performing error analysis on each simulation result and a hardware-in-loop test result; according to the method, traditional manual testing work is replaced, parameter identification and electromechanical transient modeling testing can be automatically carried out, result data are automatically stored, and the precision and collection efficiency of a new energy electromechanical simulation model are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the field of municipal construction, and particularly relates to a low-ride fault impedance identification system and method for a doubly-fed fan electromechanical model based on the dichotomy method. Background Art

[0002] In fields such as urban rail, water conservancy tunnels, and utility tunnels, circular shafts are widely used because of their good structural stress and small footprint of the required construction site; in recent years, with the acceleration of urbanization, circular shafts are all developing in the direction of large diameters. However, due to the great difficulty in coordinating the expropriation and demolition of the required site for urbanization construction or the limitations of the equipment itself, the gantry crane track beam inevitably spans the circular shaft, resulting in great construction difficulty and high safety risks; therefore, it is necessary to design a combined track beam structure for the gantry crane spanning the shaft to solve the above problems. Therefore, it is necessary to design a low-ride fault impedance identification system and method for a doubly-fed fan electromechanical model based on the dichotomy method to solve the above problems. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a low-ride fault impedance identification system and method for a doubly-fed fan electromechanical model based on the dichotomy method, aiming to solve the problem that in the prior art, due to the limitation of the site or the large width and span of the foundation pit, the track beam cannot be set on the ground; it has the characteristic that the gantry crane track beam of the large-diameter circular shaft can be arranged across the shaft.

[0004] To achieve the above technical effects, the technical solution adopted by the present invention is as follows: A low-ride fault impedance identification system for a doubly-fed fan electromechanical model based on the dichotomy method includes an identification module and a comparison module; the identification module is used for identifying the falling impedance of the electromechanical model of the new energy controller. The identification of the falling impedance is based on the hardware-in-the-loop measured data, and the value of the falling impedance of the identification model is automatically modified by writing an optimized identification algorithm of the binary search method until the falling voltage identification accuracy requirement is met and the optimal falling impedance is automatically output. The comparison module is used for the electromechanical transient modeling test of the new energy, verifying the correctness of the falling impedance in the identification module, automatically performing the electromechanical transient simulation by setting different test conditions, and analyzing the error between each simulation result and the hardware-in-the-loop test result.

[0005] Preferably, the identification module includes function modules of data call, parameter modification, model operation, result comparison, and parameter output; the data call is the hardware-in-the-loop simulation result data read by PSASP; the parameter modification is to modify the falling impedance in the identification model; the model operation is the operation of the electromechanical identification model; the result comparison is the difference comparison between the hardware-in-the-loop simulation result and the simulation result of the falling impedance identification model; the parameter output is to output the optimal falling impedance parameter obtained through identification.

[0006] Preferably, the comparison module includes function modules for parameter import, data call, operating condition switching, model operation, and error analysis; parameter import is to import the optimal fault impedance value output by the identification module into the electromechanical transient model; data call is the measured simulation result data; operating condition switching is to switch the test operating condition of the electromechanical transient model to the same test operating condition as the hardware-in-the-loop simulation model; model operation is to control the simulation operation of the electromechanical transient model; error analysis is to perform error analysis on the error calculation between the measured result and the simulation result of the electromechanical transient model.

[0007] Preferably, the above-mentioned low-voltage ride-through fault impedance identification method for the DFIG-based electromechanical model includes the following steps: S1. Conduct parameter identification, including: Build an identification model in the PSASP software and determine the parameters to be identified as the fault resistance R and fault reactance X during the ride-through period; Read and write the key parameter data tables in the PSASP database; in order to automate the simulation process, it is first necessary to be able to efficiently read and write the key parameter data tables stored in the PSASP database; Calculate the equivalent parameters of the user-specified node; The system needs to provide nodes for the user to select based on the original data read from the database and automatically calculate the equivalent parameters of the user-specified node to provide accurate data support for the simplification of the model; Automatically execute file opening and simulation experiment running; the process of the simulation experiment is cumbersome, and it is necessary to frequently open files and run multiple simulations. The requirement of this project is to automate this process, from opening the simulation file to executing the experiment, making the entire simulation analysis more efficient and stable, and reducing the errors caused by human intervention.

[0008] Complete the full low-voltage ride-through simulation process; Visualization of experimental results and key point annotation; after the simulation experiment is completed, the system should automatically draw the result curve and clearly mark the key points on the chart; through this function, researchers can more intuitively analyze the experimental results and improve the efficiency and accuracy of data analysis; S2. Conduct new energy electromechanical transient modeling tests.

[0009] Preferably, the specific method for automatically executing file opening and simulation experiment running is as follows: Conduct database initialization: On the premise that the username and password of the MySQL database are unknown, first shield its user authentication mechanism and use threads for operation; after confirming that the database permissions are open, use pymysql to implement the functions of adding, deleting, modifying, and querying data tables; at the same time, install a visual database software such as Navicat to view the data tables; Model file processing: Delete all old model files in the folder to accurately find the model file corresponding to the current model; open the current model and automatically generate new model files; Automation program startup and operation: Before starting the automation program, close the original model file; use pywinauto to start the automation tool to open the PSASP software; traverse the window layer by layer through shortcut key operations, find the module for inputting the file path, type in the path and then open the model.

[0010] Preferably, when completing the complete low-voltage ride-through simulation process, it is necessary to first calculate the grid-side voltage that can meet the requirements of the machine terminal voltage according to the active power output and reactive power output characteristics of the generator, and then use the optimization algorithm of the binary search method to automatically search for the short-circuit parameter settings that meet the operating conditions, and continuously compare the results of the hardware-in-the-loop test with the results of each simulation of the model in PSASP until the optimal drop impedance value is automatically output to meet the identification accuracy requirements, and finally complete the corresponding low-voltage ride-through experiment.

[0011] Preferably, the specific method for completing the complete low-voltage ride-through simulation process is as follows: First, perform model and database settings: After opening the PSASP model, first perform a power flow calculation, and then close the window; search for the database corresponding to the model in the data folder, and traverse all databases to find the database containing the model; read all operating conditions of this experiment, and sort according to P 、 Q values to save the time for adjusting the voltage; Perform voltage setting and adjustment: Set the initial R 、 X values for each operating condition, R 、 X The values of R and X are the drop resistance and drop reactance at the target drop voltage when the new energy unit is no-load; calculate P and Q according to the impedance voltage division at no-load, and convert them into a dictionary and pass it to the subsequent function; set the reactance and resistance ranges for high and low rides, set the voltage according to P 、 Q values, read and update the values in the relevant data tables to ensure that P 、 Q and the machine terminal voltage meet the requirements; Perform fault setting and high and low ride-through experiments: Set five groups of faults and calculate the drop voltage of each fault.

[0012] Preferably, the specific method for performing fault setting and high and low ride-through experiments is as follows: Set faults at equal intervals within a set range, select two points that can cover the target voltage as the boundaries for the next search; Perform simulation tests for each set of reactance and resistance values and collect the results of the simulation tests; Make a conditional determination on the simulation results: if the simulation results meet the requirements of the preset accuracy, record the optimal solution; otherwise, compare the target value with the closest result. If the target value is less than the closest result, update the lower bound of the search range; if the target value is greater than the closest result, update the upper bound of the search range; if the target value is close to the closest result, adjust the search range to near the current result, generate a new arithmetic progression based on the adjusted search range, and prepare for the next iteration; If the maximum number of iterations is reached, end and return the current optimal solution. If the maximum number of iterations is not reached, return to the step of generating the arithmetic progression and continue the iteration.

[0013] Preferably, the specific method for visualizing the experimental results and annotating key points is as follows: Collect experimental result data from the simulation system, including curve data under all simulation conditions; save the collected data to a CSV file; ensure the accuracy of the data format and content. Organize and manage the exported files to make the file paths and naming rules consistent for subsequent processing; Draw an experimental curve graph: read the experimental result data from the CSV file and convert it into a data frame format suitable for plotting; use a plotting tool to generate a curve graph of the experimental results, draw the experimental curves for each condition, ensure that the curves for different conditions can be distinguished, annotate the key points in the curve graph, and highlight the important experimental data points.

[0014] Preferably, the specific method for conducting new energy electromechanical transient modeling tests is as follows: For the parameters obtained in the identification module, use the pymysql module to access the parameters in the database and automatically transmit the identified parameters to the electromechanical transient model through Python; Set a test condition switching button through Python and use the pyautogui module to control the electromechanical transient model in PSASP to perform automatic simulation calculations; Compare and analyze the errors between the electromechanical transient simulation calculation results and the hardware-in-the-loop simulation results.

[0015] The beneficial effects of the present invention are as follows: 1. The present invention utilizes the voltage dip impedance automatic identification technology, which can not only save time but also achieve accurate identification of the dip impedance, realizing batch automatic identification and accurate setting of the impedance of the new energy electromechanical simulation model under multiple voltage dip conditions, greatly improving the accuracy of multi-condition setting and data acquisition efficiency of the new energy electromechanical simulation model.

[0016] 2. The initial impedance of the present invention is set to ensure that the algorithm can accurately simulate the behavior of the power grid under different voltage conditions. In the algorithm, the ratio of the initial resistance to the reactance is set to 1:10, which is in line with the actual line and helps to simulate the voltage drop and current flow in the actual power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart of the low-voltage ride-through fault impedance identification technology for the doubly-fed wind turbine electromechanical model of the present invention; Figure 2 is an electromechanical transient simulation model diagram in an embodiment of the present invention; Figure 3 is a comparison diagram of the simulated and measured terminal voltages of the low-voltage ride-through fault impedance identification technology for the doubly-fed wind turbine electromechanical model in an embodiment of the present invention under the conditions of P = 0.9Pn, Q = 0.3Qn, U = 0.5Un, and three-phase low-voltage ride-through at 1.214 s; Figure 4 is a comparison diagram of the simulated and measured active currents of the low-voltage ride-through fault impedance identification technology for the doubly-fed wind turbine electromechanical model in an embodiment of the present invention under the conditions of P = 0.9Pn, Q = 0.3Qn, U = 0.5Un, and three-phase low-voltage ride-through at 1.214 s; Figure 5 is a comparison diagram of the simulated and measured reactive currents of the low-voltage ride-through fault impedance identification technology for the doubly-fed wind turbine electromechanical model in an embodiment of the present invention under the conditions of P = 0.9Pn, Q = 0.3Qn, U = 0.5Un, and three-phase low-voltage ride-through at 1.214 s; Figure 6 is a comparison diagram of the simulated and measured active powers of the low-voltage ride-through fault impedance identification technology for the doubly-fed wind turbine electromechanical model in an embodiment of the present invention under the conditions of P = 0.9Pn, Q = 0.3Qn, U = 0.5Un, and three-phase low-voltage ride-through at 1.214 s; Figure 7 is a comparison diagram of the simulated and measured reactive powers of the low-voltage ride-through fault impedance identification technology for the doubly-fed wind turbine electromechanical model in an embodiment of the present invention under the conditions of P = 0.9Pn, Q = 0.3Qn, U = 0.5Un, and three-phase low-voltage ride-through at 1.214 s. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Embodiment 1: As Figure 1 shown, the low-voltage ride-through fault impedance identification system for the doubly-fed wind turbine electromechanical model based on the bisection method includes an identification module and a comparison module. The identification module is used for identifying the drop impedance of the new energy controller electromechanical model. The identification of the drop impedance is based on the measured data of hardware-in-the-loop. By writing an optimized identification algorithm of the bisection search method, the value of the drop impedance of the identification model is automatically modified until the drop voltage identification accuracy requirement is met and the optimal drop impedance is automatically output. The comparison module is used for new energy electromechanical transient modeling tests to verify the correctness of the drop impedance in the identification module. By setting different test conditions, it automatically performs electromechanical transient simulations and analyzes the errors between the simulation results of each time and the results of the hardware-in-the-loop tests.

[0019] Preferably, the identification module includes function modules for data calling, parameter modification, model operation, result comparison, and parameter output; data calling is the data of the hardware-in-the-loop simulation results read by PSASP; parameter modification is to modify the drop impedance in the identification model; model operation is the operation of the electromechanical identification model; result comparison is the comparison of the differences between the hardware-in-the-loop simulation results and the simulation results of the drop impedance identification model; parameter output is to output the optimal drop impedance parameters obtained through identification.

[0020] Preferably, the comparison module includes function modules for parameter import, data calling, working condition switching, model operation, and error analysis; parameter import is to import the optimal drop impedance value output by the identification module into the electromechanical transient model; data calling is the measured simulation result data; working condition switching is to switch the test working condition of the electromechanical transient model to the same test working condition as the hardware-in-the-loop simulation model; model operation is to control the simulation operation of the electromechanical transient model; error analysis is to perform error analysis by calculating the errors between the measured results and the simulation results of the electromechanical transient model.

[0021] Embodiment 2: The above-mentioned method for identifying the low-voltage ride-through fault impedance of the doubly-fed wind turbine electromechanical model based on the dichotomy includes the following steps: S1. Conduct parameter identification, including: Build an identification model in the PSASP software and determine the parameters to be identified as the drop resistance R and drop reactance X during the crossing period; Read and write the key parameter data tables in the PSASP database; in order to realize the automation of the simulation process, it is first necessary to be able to efficiently read and write the key parameter data tables stored in the PSASP database; Calculate the equivalent parameters of the user-specified node; The system needs to provide nodes for the user to select based on the original data read from the database and automatically calculate the equivalent parameters of the user-specified node to provide accurate data support for the simplification of the model; Automatically execute file opening and simulation experiment running; the process of the simulation experiment is cumbersome and requires frequent file opening and multiple simulation runs. The requirement of this project is to automate this process, from opening the simulation file to executing the experiment process, making the entire simulation analysis more efficient and stable and reducing the errors caused by human intervention.

[0022] Complete the complete low-voltage ride-through simulation process; Visualization of experimental results and key point annotation; after the simulation experiment is completed, the system should automatically draw the result curve and clearly mark the key points in the chart; through this function, researchers can analyze the experimental results more intuitively and improve the efficiency and accuracy of data analysis; S2, conduct new energy electromechanical transient modeling tests.

[0023] Preferably, the specific method for automatically executing file opening and simulation experiment running is as follows: Perform database initialization: On the premise that the username and password of the MySQL database are unknown, first shield its user authentication mechanism and use threads for operations; after confirming that the database permissions are open, use pymysql to implement the functions of adding, deleting, modifying, and querying data tables; at the same time, install a visual database software such as Navicat to view the data tables; Model file processing: Delete all old model files in the folder to accurately find the model file corresponding to the current model; open the current model and automatically generate new model files; Automated program startup and operation: Before starting the automated program, close the original model file; use pywinauto to start the automated tool to open the PSASP software; traverse the window layer by layer through shortcut key operations, find the module for inputting the file path, type in the path, and then open the model.

[0024] Preferably, when completing the complete low-voltage ride-through simulation process, it is necessary to first calculate the grid-side voltage that can meet the requirements of the machine terminal voltage according to the active power output and reactive power output characteristics of the generator, and then use the optimization algorithm of the binary search method to automatically search for the short-circuit parameter settings that meet the operating conditions, continuously compare the results of the hardware-in-the-loop test with the simulation results of the model in PSASP each time until the optimal drop impedance value is automatically output to meet the identification accuracy requirements, and finally complete the corresponding low-voltage ride-through experiment.

[0025] Preferably, the specific method for completing the complete low-voltage ride-through simulation process is as follows: First, perform model and database settings: After opening the PSASP model, first perform a power flow calculation and then close the window; go to the data folder to find the database corresponding to the model, traverse all databases to find the database containing the model; read all the operating conditions of this experiment and sort according to P 、 Q values to save the time for adjusting the voltage; Perform voltage setting and adjustment: Set the initial R 、 X values for each operating condition, R 、X The value is the drop resistance and drop reactance of the target drop voltage when the new energy unit is no-load; according to the impedance voltage division when no-load, calculate R and X , and convert it into a dictionary and pass it to the subsequent function; set the reactance and resistance range when high and low wear, according to P 、 Q Set the voltage value, read and update the value in the relevant data sheet, and ensure P 、 Q The terminal voltage meets the requirements; Conduct fault setting and high and low ride-through experiments: set five groups of faults and calculate the drop voltage for each fault.

[0026] like Figure 2 As shown in the electromechanical simulation model, the per-unit impedance is set according to the manufacturer's requirements: T1 = 0.06, T2 = 0.105, T3 = 0.105, T4 = 0.18, L1 = 0.216. When the short circuit ratio is 1.5, P = 0.9P n ,Q=0.3Q n 、U=0.5U n , three-phase low-voltage breakdown 1.214s, three-phase short circuit fault occurs at bus 71. According to the impedance voltage division of the no-load test, there is ;in Z L1 =0.216, the short-circuit impedance can be calculated Z s .

[0027] because ; and the initial impedance is based on X= 10 R The setting is consistent with the actual circuit, so the initial short-circuit resistance can be calculated separately R and short-circuit reactance X and set the initial short-circuit resistance R and short-circuit reactance X Convert it into a dictionary and pass it to subsequent functions.

[0028] Furthermore, the reactance and resistance ranges during low wear are set, with the initial resistance R As the upper limit of resistance R max , initial reactance X As the upper limit of reactance X max , with 1 / 50 R max As the lower limit of resistance R min , with 1 / 50 X max As the lower limit of reactance Xmin .

[0029] Further, set the voltage according to P 、 Q values, read and update the values in the relevant data tables to ensure that P 、 Q and the terminal voltage meet the requirements.

[0030] Preferably, the specific method for performing fault setting and high and low crossing experiments is as follows: Set faults at equal intervals within the set range, and use the binary search method to select two points that can cover the target voltage as the boundaries for the next search; Within the set range X min ，X max , R min ，R max set faults at equal intervals, perform simulation tests on each set of reactance and resistance values, and collect the results of the simulation tests; Make a conditional judgment on the simulation results: if the simulation results meet the requirements of the preset accuracy, record the optimal solution; otherwise, compare the target value with the closest result. If the target value is less than the closest result, update the lower bound of the search range; if the target value is greater than the closest result, update the upper bound of the search range; if the target value is close to the closest result, adjust the search range to near the current result, generate a new arithmetic sequence according to the adjusted search range, and prepare for the next iteration; If the maximum number of iterations is reached, end and return the current optimal solution. If the maximum number of iterations is not reached, return to the step of generating the arithmetic sequence and continue the iteration.

[0031] Preferably, the specific method for visualizing the experimental results and annotating key points is as follows: Collect the experimental result data from the simulation system, including the curve data under all simulation conditions; save the collected data to a CSV file; ensure the accuracy of the data format and content. Organize and manage the exported files to make the file paths and naming rules consistent for subsequent processing; Draw the experimental curve graph: read the experimental result data from the CSV file and convert it into a data frame format suitable for plotting; use the plotting tool to generate the curve graph of the experimental results, draw the experimental curves for each condition, ensure that the curves for different conditions can be distinguished, annotate the key points in the curve graph, and highlight the important experimental data points.

[0032] Preferably, the specific method for performing new energy electromechanical transient modeling tests is as follows: The parameters obtained in the identification module are used to access the parameters in the database by means of the pymysql module, and the identified parameters are automatically transmitted to the electromechanical transient model through Python; Set the test condition switching button through Python, and use the pyautogui module to control the electromechanical transient model in PSASP to perform automatic simulation calculations; Compare and analyze the errors between the results of the electromechanical transient simulation calculation and the hardware-in-the-loop simulation results.

[0033] Example 3: Figures 3 - 7 It is the waveform diagram simulated under the conditions of P = 0.9P n , Q = 0.3Q n , U = 0.5U n , and the three-phase low voltage ride-through of 1.214 s. Under this condition, the active power of the power generation unit is set to 0.9P n , the reactive power is 0.3Q n , and it runs stably for 1.0 s. A short-circuit fault is set at the bus 71 position to cause the terminal bus voltage of the photovoltaic power generation unit to drop to 0.5U n , which lasts for 1.214 s, and the voltage recovers until the unit runs stably. Record the terminal voltage, active current, reactive current, active power, and reactive power.

Claims

1. A low-voltage ride-through fault impedance identification system for the electromechanical model of a doubly-fed wind turbine based on the dichotomy method, characterized in that, It includes an identification module and a comparison module; the identification module is used for identifying the falling impedance of the electromechanical model of the new energy controller. The identification of the falling impedance is based on the measured data of hardware-in-the-loop. By writing an optimized identification algorithm of the binary search method, the value of the falling impedance of the identification model is automatically modified until the falling voltage identification accuracy requirement is met and the optimal falling impedance is automatically output. The comparison module is used for the electromechanical transient modeling test of new energy to verify the correctness of the falling impedance in the identification module. By setting different test conditions, the electromechanical transient simulation is automatically carried out, and the error analysis is performed on the simulation results of each time and the hardware-in-the-loop test results.

2. The impedance identification system for low voltage ride through faults of the doubly-fed wind turbine electromechanical model based on the dichotomy method according to claim 1, wherein, The identification module includes function modules of data calling, parameter modification, model operation, result comparison, and parameter output; data calling is the hardware-in-the-loop simulation result data read by PSASP; parameter modification is to modify the falling impedance in the identification model. Model operation is the operation of the electromechanical identification model; result comparison is the difference comparison between the hardware-in-the-loop simulation result and the simulation result of the falling impedance identification model; parameter output is to output the optimal falling impedance parameter obtained through identification.

3. The low-voltage ride-through fault impedance identification system for the doubly-fed wind turbine electromechanical model based on the dichotomy method according to claim 1, wherein, The comparison module includes function modules of parameter import, data calling, working condition switching, model operation, and error analysis; parameter import is to import the optimal falling impedance value output by the identification module into the electromechanical transient model. Data calling is the measured simulation result data; working condition switching is to switch the test working condition of the electromechanical transient model to the same test working condition as the hardware-in-the-loop simulation model. Model operation is to control the simulation operation of the electromechanical transient model. Error analysis is to perform error analysis on the error calculation between the measured result and the simulation result of the electromechanical transient model.

4. The method for identifying the low-ride-through fault impedance of the doubly-fed wind turbine electromechanical model based on the dichotomy according to any one of claims 1-3, characterized in that It includes the following steps: S1. Conduct parameter identification, including: Build an identification model in the PSASP software and determine that the parameters to be identified are the falling resistance R and the falling reactance X during the crossing period. Read and write the key parameter data table in the PSASP database. Calculate the equivalent parameters of the user-specified node. Open the automated execution file and run the simulation experiment. Complete the complete low-voltage ride-through simulation process. Visualization of the experimental results and key point marking. S2. Conduct the electromechanical transient modeling test of new energy.

5. The impedance identification method for low voltage ride through fault of the doubly-fed wind turbine electromechanical model based on the dichotomy method according to claim 4, characterized in that The specific method of opening the automated execution file and running the simulation experiment is as follows: Conduct database initialization: On the premise that the username and password of the MySQL database are unknown, first shield its user authentication mechanism and use threads for operation; after confirming that the database permissions are opened, use pymysql to implement the functions of adding, deleting, modifying, and querying data tables; at the same time, install visualization database software to view the data tables. Model file processing: Delete all the old model files in the folder to accurately find the model file corresponding to the current model; open the current model and automatically generate a new model file. Automated program startup and operation: Close the original model file before starting the automated program; use pywinauto to start the automated tool to open the PSASP software; through shortcut key operations, traverse the window layer by layer to find the module for inputting the file path, type in the path, and then open the model.

6. The method for identifying the low-ride-through fault impedance of the doubly-fed wind turbine electromechanical model based on the dichotomy according to claim 4, characterized in that When completing the full low-voltage ride-through simulation process, it is necessary to calculate the grid-side voltage that can meet the requirements of the terminal voltage according to the active power output and reactive power output characteristics of the generator, and then use the optimized algorithm of the binary search method to automatically search for the short-circuit parameter settings that meet the operating conditions. Continuously compare the results of the hardware-in-the-loop test with the simulation results of the model in PSASP until the optimal drop impedance value is automatically output to meet the identification accuracy requirements, and finally complete the corresponding low-voltage ride-through experiment.

7. The method for identifying the low-ride-through fault impedance of the doubly-fed wind turbine electromechanical model based on the dichotomy according to claim 6, characterized in that The specific method for completing the full low-voltage ride-through simulation process is as follows: First, perform model and database settings: After opening the PSASP model, first perform a power flow calculation, and then close the window; search for the database corresponding to the model in the data folder, and traverse all databases to find the database containing the package model; read all the operating conditions of this experiment, and sort according to the P , Q values to save the time for adjusting the voltage; Perform voltage settings and adjustments: Set initial R and X values for each operating condition. The values of R and X are the drop resistance and drop reactance at the target drop voltage when the new energy unit is unloaded. According to the impedance voltage division at no load, calculate R and X , and convert them into a dictionary to pass to the subsequent function. Set the reactance and resistance ranges during high and low penetration. Set the voltage according to the P and Q values, read and update the values in the relevant data tables to ensure that P , Q and the terminal voltage meet the requirements. Perform fault settings and high and low ride-through experiments: Set five groups of faults and calculate the drop voltage for each fault.

8. The method for identifying the low-ride-through fault impedance of the doubly-fed wind turbine electromechanical model based on the dichotomy according to claim 7, characterized in that, The specific method for performing fault settings and high and low ride-through experiments is as follows: Set faults at equal intervals within the set range, select two points that can cover the target voltage as the boundaries for the next search; Execute simulation tests for each set of reactance and resistance values and collect the results of the simulation tests; Make a conditional judgment on the simulation results: If the simulation results meet the requirements of the preset accuracy, record the optimal solution; Otherwise, compare the target value with the closest result. If the target value is less than the closest result, update the lower bound of the search range; If the target value is greater than the closest result, update the upper bound of the search range; If the target value is close to the closest result, adjust the search range to near the current result, generate a new arithmetic sequence based on the adjusted search range, and prepare for the next iteration; If the maximum number of iterations is reached, end and return the current optimal solution. If the maximum number of iterations is not reached, return to the step of generating the arithmetic sequence and continue the iteration.

9. The impedance identification method for low voltage ride-through fault of the doubly-fed induction generator electromechanical model based on the dichotomy according to claim 4, wherein The specific method for visualizing the experimental results and annotating key points is as follows: Collect the experimental result data from the simulation system, including the curve data under all simulation conditions; save the collected data to a CSV file; organize and manage the exported files to make the file paths and naming rules consistent; Draw the experimental curve graph: Read the experimental result data from the CSV file and convert it into a data frame format suitable for plotting; use the plotting tool to generate the curve graph of the experimental results, draw the experimental curves for each condition, ensure that the curves of different conditions can be distinguished, annotate the key points in the curve graph, and highlight the important experimental data points.

10. The method for identifying the low-ride-through fault impedance of the doubly-fed wind turbine electromechanical model based on the dichotomy according to claim 4, wherein The specific method for conducting new energy electromechanical transient modeling tests is as follows: For the parameters obtained in the identification module, use the pymysql module to access the parameters in the database and automatically transfer the identified parameters to the electromechanical transient model through Python; Set the test condition switching button through Python and use the pyautogui module to control the electromechanical transient model in PSASP to perform automatic simulation calculations; Compare and analyze the errors between the electromechanical transient simulation calculation results and the hardware-in-the-loop simulation results.

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