Method for checking electromechanical transient model of new energy unit and device for identifying control parameters of new energy unit

Abstract

The application relates to a method for verifying an electromechanical transient model of a new energy unit and a control parameter identification method, device and equipment thereof, the method comprising the following steps: acquiring the equipment type of the new energy unit, and acquiring operation data required for electromechanical transient operation according to the equipment type; constructing a measured system for electromechanical transient operation of the new energy unit according to the equipment type, and acquiring first measured data by controlling the measured system to operate according to the operation data; processing the first measured data to obtain sequence component data; acquiring characteristic-related initial data, and constructing a characteristic curve set according to the characteristic-related initial data and the sequence component data of all operation conditions; acquiring the control strategy of the corresponding new energy unit according to the equipment type, and acquiring parameter identification data from the characteristic curve set according to the control strategy; and identifying the parameter identification data by using a least square method to obtain optimal control parameters matched with the first measured data. The method realizes identification of control parameters of model simulation.

Description

Technical Field

[0001] This application relates to the field of power system simulation technology, and in particular to a method for verifying the electromechanical transient model of a new energy unit and a method, device and equipment for identifying its control parameters. Background Technology

[0002] In the fields of power system automation, new energy power generation, and power electronics and electric drives, the experimental modeling and parameter identification of electromechanical transients of new energy units is an important research direction. With the widespread application of new energy units such as wind power, solar power, and electrochemical energy storage, how to accurately simulate the electromechanical transient behavior of these units and improve their operating efficiency and safety has become a significant technical challenge.

[0003] In existing technologies, the electromechanical transient modeling of new energy units typically verifies the model's accuracy by setting a limited number of different operating conditions. For example, the model's accuracy is verified by setting different fault types, fault locations, fault durations, and different operating conditions. The rules for verifying model accuracy can refer to standards such as "NB / T31053 Verification Procedure for Electrical Simulation Models of Wind Turbine Units," "GB / T32892 Test Procedure for Models and Parameters of Photovoltaic Power Generation Systems," and "GBT44117 Test Procedure for Model Parameters of Electrochemical Energy Storage Power Stations."

[0004] However, existing methods for validating electromechanical transient models of new energy generating units still have shortcomings. These shortcomings include: First, the lack of standardized validation methods. For example, there is currently a lack of unified standards and specifications for validating electromechanical transient models of new energy generating units. Different technicians may use different validation methods and indicators, affecting the comparability and reliability of model validation results. Second, the validation models do not cover all operating conditions. It is necessary to verify the accuracy of the model under various operating conditions, including different fault types, fault locations, fault durations, and different operating conditions. However, it is difficult to list all possible operating conditions of new energy generating units in practice. This may lead to insufficient verification of the model's accuracy under certain specific operating conditions (such as control switching, action threshold boundaries, etc.), posing potential risks.

[0005] During model validation, comprehensive validation rules reflecting control and action threshold boundaries require meticulous simulation of hundreds or thousands of test conditions. The sheer volume of sample test data renders traditional methods ineffective and unsuitable, and parameter identification for model accuracy verification increases workload exponentially. Existing methods for identifying validation model parameters become extremely labor-intensive when faced with massive amounts of test case samples, failing to meet practical needs.

[0006] Therefore, how to propose a standardized experimental modeling process and efficiently identify and verify model parameters is an important problem facing current technology. Summary of the Invention

[0007] This application provides a method for verifying the electromechanical transient model of a new energy unit and a method, device, and equipment for identifying its control parameters, which solves the technical problem that existing methods for acquiring or identifying verification model parameters are labor-intensive and cannot be used in practice.

[0008] To achieve the above objectives, this application provides the following technical solution:

[0009] On the one hand, a method for identifying control parameters of an electromechanical transient model of a new energy unit is provided, including the following steps:

[0010] Obtain the equipment type of the new energy unit, and obtain the operating data required for electromechanical transients based on the equipment type. The operating data includes operating conditions and fault configurations corresponding to the operating conditions. The fault configurations include active power, reactive power, fault type, and voltage dip / rise series parameters.

[0011] A test system for electromechanical transient operation of the new energy unit is constructed according to the equipment type. The operation of the test system is controlled according to the operation data to obtain the first test data for electromechanical transient operation of the new energy unit corresponding to each of the operating conditions. The first test data includes three-phase voltage and three-phase current.

[0012] The first measured data is processed to obtain sequence component data corresponding to the duration of voltage drop before the fault and after the fault during the transient process. The sequence component data includes sequence component voltage, sequence component active power, sequence component reactive power, sequence component active current and sequence component reactive current.

[0013] Obtain characteristic-related initial data, and construct a characteristic curve set based on the characteristic-related initial data and the sequence component data of all the operating conditions. The characteristic curve set includes voltage characteristic-related curves and initial value characteristic-related curves.

[0014] According to the equipment type, obtain the control strategy corresponding to the new energy unit, and according to the control strategy, obtain parameter identification data from the characteristic curve set;

[0015] The least squares method is used to identify the parameter identification data to obtain the optimal control parameters that match the first measured data.

[0016] Preferably, obtaining parameter identification data from the characteristic curve set according to the control strategy includes:

[0017] The control method for fault ride-through of the new energy unit during transient operation is determined based on the control strategy.

[0018] Extract the voltage characteristic correlation curve and the initial value characteristic correlation curve corresponding to the control method from the characteristic curve set;

[0019] Parameter identification data corresponding to the control mode are obtained from the extracted voltage characteristic correlation curve and the initial value characteristic correlation curve.

[0020] Preferably, the control method includes active power control, active current control, reactive power control, and reactive current control.

[0021] If the control method is active power control, then the voltage characteristic correlation curve is the voltage characteristic correlation curve composed of the sequence component voltage and the sequence component active power, the initial value characteristic correlation curve is the initial value of active power composed of the initial value of the characteristic correlation initial data and the sequence component active power, and the parameter identification data includes the sequence component active power and the initial value of active power.

[0022] If the control method is active current control, then the voltage characteristic correlation curve is the voltage characteristic correlation curve composed of the sequence component voltage and the sequence component active current, the initial value characteristic correlation curve is the initial value of the active current of the characteristic correlation initial data and the sequence component active current, and the parameter identification data includes the sequence component active current and the sequence component voltage.

[0023] If the control method is a reactive power control method, then the voltage characteristic correlation curve is a voltage characteristic correlation curve composed of the sequence component voltage and the sequence component reactive power, the initial value characteristic correlation curve is an initial value characteristic correlation curve composed of the initial reactive power value of the characteristic correlation initial data and the sequence component reactive power, and the parameter identification data includes the sequence component reactive power and the initial reactive power value.

[0024] If the control method is reactive current control, then the voltage characteristic correlation curve is the voltage characteristic correlation curve composed of the sequence component voltage and the sequence component reactive current, the initial value characteristic correlation curve is the initial value of the reactive current of the characteristic correlation initial data and the sequence component reactive current, and the parameter identification data includes the sequence component reactive current and the sequence component voltage.

[0025] Preferably, the least squares method is used to identify the parameter identification data to obtain the optimal control parameters that match the first measured data, including:

[0026] The parameter identification data were fitted using the least squares method to obtain a linear regression equation;

[0027] The predicted value is obtained based on the linear regression equation. The linear regression equation is optimized with the goal of minimizing the mean square error between the predicted value and the corresponding data in the first measured data, so as to obtain the optimal linear regression equation.

[0028] The optimal control parameters that match the first measured data are obtained from the optimal linear regression equation.

[0029] Preferably, the test system includes a controller, an input / output connection board, and a power grid simulator corresponding to the device type, and the controller is connected to the power grid simulator through the input / output connection board.

[0030] Preferably, the first measured data is processed to obtain sequence component data corresponding to the duration of voltage drop before and after the fault during the transient process, including:

[0031] The first measured data is filtered, denoised, and outlier removed to obtain processed data.

[0032] The second measured data is obtained by acquiring data on the duration of voltage drop before and after the fault during the transient process from the processed data.

[0033] The second measured data is processed using Fast Fourier Transform and Positive / Negative Sequence Transform to obtain sequence component data corresponding to the duration of voltage drop before and after the fault during the transient process.

[0034] On the other hand, a method for verifying the electromechanical transient model of a new energy unit is provided, including the following steps:

[0035] Obtain the electromechanical transient model and equipment type of the new energy unit;

[0036] Based on the equipment type, obtain the operating data required for electromechanical transients, and based on the equipment type, use the control parameter identification method of the electromechanical transient model of the new energy unit described above to obtain the optimal control parameters and the first measured data corresponding to the operating data;

[0037] The electromechanical transient model is simulated based on the operating data and the optimal control parameters to obtain simulation data.

[0038] The first measured data is compared with the simulation data to obtain a comparison result; based on the comparison result, it is determined whether the electromechanical transient model meets the model standard.

[0039] On the other hand, a control parameter identification device for the electromechanical transient model of a new energy unit is provided, including a first data acquisition module, a second data acquisition module, a data processing module, a curve construction module, a data extraction module, and a parameter identification module;

[0040] The first data acquisition module is used to acquire the equipment type of the new energy unit, and to acquire the operating data required for electromechanical transients according to the equipment type. The operating data includes operating conditions and fault configurations corresponding to the operating conditions. The fault configurations include active power, reactive power, fault type and voltage drop / rise series parameters.

[0041] The second data acquisition module is used to construct a test system for electromechanical transient operation of the new energy unit according to the equipment type, control the operation of the test system according to the operation data, and acquire first test data for electromechanical transient operation of the new energy unit corresponding to each of the operating conditions. The first test data includes three-phase voltage and three-phase current.

[0042] The data processing module is used to process the first measured data to obtain sequence component data corresponding to the duration of voltage drop before the fault and after the fault during the transient process. The sequence component data includes sequence component voltage, sequence component active power, sequence component reactive power, sequence component active current and sequence component reactive current.

[0043] The curve construction module is used to acquire characteristic-related initial data and construct a characteristic curve set based on the characteristic-related initial data and the sequence component data of all the operating conditions. The characteristic curve set includes voltage characteristic-related curves and initial value characteristic-related curves.

[0044] The data extraction module is used to obtain the control strategy corresponding to the new energy unit according to the equipment type, and to obtain parameter identification data from the characteristic curve set according to the control strategy;

[0045] The parameter identification module is used to identify the parameter identification data using the least squares method to obtain the optimal control parameters that match the first measured data.

[0046] Preferably, the parameter identification module is further configured to fit the parameter identification data using the least squares method to obtain a linear regression equation; obtain a predicted value based on the linear regression equation; optimize the linear regression equation with the goal of minimizing the mean square error between the predicted value and the corresponding data in the first measured data to obtain an optimal linear regression equation; and obtain the optimal control parameters that match the first measured data from the optimal linear regression equation.

[0047] On the other hand, a terminal device is provided, including a processor and a memory;

[0048] The memory is used to store program code and transmit the program code to the processor;

[0049] The processor is used to execute the control parameter identification method of the electromechanical transient model of the new energy unit as described above, according to the instructions in the program code.

[0050] The present invention discloses a verification method for the electromechanical transient model of the new energy generating unit and a method, apparatus, and equipment for identifying its control parameters. The control parameter identification method for the electromechanical transient model includes obtaining the equipment type of the new energy generating unit and, based on the equipment type, obtaining the operating data required for electromechanical transient testing. The operating data includes operating conditions and corresponding fault configurations. The fault configurations include active power, reactive power, fault type, and voltage dip / rise parameters. A test system for electromechanical transient operation of the new energy generating unit is constructed based on the equipment type. The test system is controlled based on the operating data to obtain the first measured data for electromechanical transient operation of the new energy generating unit corresponding to each operating condition. The first measured data includes three-phase voltage and three-phase... Phase current; The first measured data is processed to obtain sequence component data corresponding to the duration of voltage drop before the fault and after the fault during the transient process. The sequence component data includes sequence component voltage, sequence component active power, sequence component reactive power, sequence component active current, and sequence component reactive current; Characteristic-related initial data are obtained, and a characteristic curve set is constructed based on the characteristic-related initial data and the sequence component data of all operating conditions. The characteristic curve set includes voltage characteristic-related curves and initial value characteristic-related curves; The control strategy of the corresponding new energy unit is obtained according to the equipment type, and parameter identification data is obtained from the characteristic curve set according to the control strategy; The least squares method is used to identify the parameter identification data to obtain the optimal control parameters that match the first measured data.

[0051] As can be seen from the above technical solutions, this application has the following advantages: the control parameter identification method of the electromechanical transient model of the new energy unit realizes the identification of the control parameters for model simulation, which solves the technical problem that the existing methods of obtaining or identifying and verifying model parameters are labor-intensive and cannot be used in practice.

[0052] The verification method for the electromechanical transient model of this new energy unit verifies the accuracy of the model under various operating conditions by identifying the optimal control parameters using the control parameter identification method for the electromechanical transient model. This ensures the accuracy of the electromechanical transient model under various operating conditions, improving its reliability and safety. Furthermore, the verification method for the electromechanical transient model of this new energy unit guarantees the comparability and reliability of the verification results, improving the efficiency and accuracy of model building.

[0053] The control parameter identification device for the electromechanical transient model of the new energy unit identifies the control parameters of the new energy unit through a first data acquisition module, a second data acquisition module, a data processing module, a curve construction module, a data extraction module, and a parameter identification module. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart illustrating the steps of the control parameter identification method for the electromechanical transient model of a new energy unit as described in this application embodiment;

[0056] Figure 2 This is a flowchart illustrating the steps of the verification method for the electromechanical transient model of the new energy unit described in this application embodiment;

[0057] Figure 3 This is a schematic diagram of the control parameter identification device for the electromechanical transient model of the new energy unit described in this application embodiment;

[0058] Figure 4 This is a schematic diagram of the terminal device described in an embodiment of this application. Detailed Implementation

[0059] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] In the description of the embodiments of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0061] In the embodiments of this application, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application according to the specific circumstances.

[0062] Explanation of patent terminology in this application:

[0063] A power grid simulator is a device that can simulate the actual operating conditions of a power grid. It uses digital or physical models to represent the characteristics of a power system and identifies problems in the actual power system by studying these models. Simulators are used to simulate the planning, construction, and operation phases of a power grid, helping to thoroughly understand its characteristics, verify prevention and control strategies, and thus ensure the scientific planning and safe operation of the power grid.

[0064] This application provides a method for verifying the electromechanical transient model of a new energy power unit, as well as a method, apparatus, and equipment for identifying its control parameters. This solves the technical problem that existing methods for acquiring or identifying verification model parameters are labor-intensive and impractical. The method, apparatus, and equipment for verifying the electromechanical transient model of a new energy power unit can construct a test system according to different equipment types of new energy power units, propose standardized verification steps for the electromechanical transient model, and are applicable to various scenarios such as wind, solar, and energy storage; they are also suitable for parameter identification and simulation under massive operating conditions.

[0065] Example 1:

[0066] Figure 1 This is a flowchart illustrating the steps of the control parameter identification method for the electromechanical transient model of a new energy unit as described in an embodiment of this application.

[0067] like Figure 1 As shown in the figure, this application provides a method for identifying control parameters of an electromechanical transient model of a new energy unit, including the following steps:

[0068] S1. Obtain the equipment type of the new energy unit, and obtain the operating data required for electromechanical transients based on the equipment type. The operating data includes the operating conditions and the fault configurations corresponding to the operating conditions. The fault configurations include active power, reactive power, fault type, and voltage dip / rise series parameters.

[0069] It should be noted that step S1 involves first obtaining the equipment type of the new energy unit, and then selecting operating data based on the different equipment types. The operating data includes operating conditions, fault configurations, and modeling accuracy requirements. In this embodiment, if the equipment type is a wind turbine, the operating data is configured according to the standard "NB / T 31053-2021 Verification Procedure for Electrical Simulation Model of Wind Turbine"; if the equipment type is a dynamic reactive power compensation SVG for a wind farm, the operating data is configured according to the standard "NB / T 10316-2019 Test Specification for Grid Connection Performance of Dynamic Reactive Power Compensation Device for Wind Farm"; if the equipment type is a photovoltaic inverter, the operating data is configured according to the standard "GB / T 32892-2016 Test Procedure for Model and Parameters of Photovoltaic Power Generation System"; if the equipment type is a dynamic reactive power compensation SVG for a photovoltaic power station, the operating data is configured according to the standard "GB / T 34931-2017 Technical Specification for Testing Reactive Power Compensation Devices in Photovoltaic Power Stations"; if the equipment type is an energy storage converter, the operating data is configured according to the standard "GBT44117 Test Procedure for Model Parameters of Electrochemical Energy Storage Power Station". Operational data can be obtained from the technical specifications, design parameters, and control strategy documents of the new energy generating units. Understanding the basic structure, electrical parameters, and control methods of these units provides a foundation for subsequent parameter identification. Specifically, fault configuration needs to be performed according to the equipment type and relevant standards of the new energy generating unit. Fault configuration data includes the magnitude of active power, the magnitude of reactive power, the fault type, and the voltage sag / surge amplitude (UT) and duration (TV) configured in ascending order. A list of voltage sag / surge amplitude (UT) and duration (TV) configured in ascending order is shown in Table 1.

[0070] Table 1 shows the voltage specifications for voltage drop testing (taking the standard "NB / T 31053-2021 Verification Procedure for Electrical Simulation Model of Wind Turbine Units" as an example).

[0071]

[0072] S2. Construct a test system for electromechanical transient operation of new energy units according to equipment type, control the operation of the test system according to the operation data, and obtain the first test data for electromechanical transient operation of new energy units corresponding to each operating condition. The first test data includes three-phase voltage and three-phase current.

[0073] It should be noted that step S2 involves first constructing a test system to acquire measured data based on the equipment type of the new energy unit obtained in step S1, and then running the test system to obtain the first measured data based on the operating data obtained in step S1. In this embodiment, if the operating conditions in Table 1 are standard requirements, the number of data points is small, making it difficult to comprehensively and accurately reflect the response of the controller in the test system. In the specific implementation of acquiring the first measured data, to refine the transient characteristics of the controller in the test system, a linear interpolation method can be used to expand the voltage drop / rise amplitude UT and voltage drop duration TV in Table 1 to expand sufficient or massive sample data. For example, a voltage fault specification UT and TV can be added between the i-th and i+1-th serial numbers in Table 1. The calculation formula for the linear interpolation method can refer to Formula 1. Other expanded operating conditions are similar, without limiting the number of data points for each operating condition, until each operating condition is considered sufficiently refined, forming an expanded fault operating condition list. The total number of data points for each operating condition is denoted as N. The expression of Formula 1 is:

[0074] .

[0075] In this embodiment, the test system includes a controller corresponding to the device type, an input / output connection board, and a power grid simulator. The controller is connected to the power grid simulator through the input / output connection board.

[0076] It should be noted that during the construction of the test system, a hardware-in-the-loop semi-physical simulation approach can be used. For example, the controller of the object under test (such as a new energy unit) can be interconnected with the power grid simulator via an input / output connection board (such as an I / O board). Specifically, the controller uses FPGA small-step nanosecond-level simulation to simulate the electrical and power electronic switches of the converter in the object under test. The three-phase voltage and three-phase current of the converter in the object under test are output and sent to the controller via the I / O board. After receiving the data, the controller generates a corresponding PWM pulse signal and sends it to the power grid simulator via the I / O board to obtain the first measured data. In this embodiment, the data can be expanded according to the data in Table 1 above to perform automated testing on the controller of the object under test. The testing process involves writing an automated test script. The script should be able to automatically configure the test running data, start the test, record data, and generate reports. The automated test script and the power grid simulator are connected via API or communication protocols (such as Modbus or TCP / IP) to achieve automated control. The power grid simulator uses automated tools to quickly switch between fault simulations and automatically execute test operating conditions in batches. The test results for each operating condition are automatically saved to the database. Comprehensive measured data is obtained based on the electrical quantities of three-phase voltage and three-phase current output by the controller. During the acquisition of measured data, the measurement data and timestamps of the measured system are recorded simultaneously to ensure the accuracy and synchronization of the data.

[0077] S3. Process the first measured data to obtain the sequence component data corresponding to the duration of voltage drop before the fault and after the fault during the transient process. The sequence component data includes sequence component voltage, sequence component active power, sequence component reactive power, sequence component active current and sequence component reactive current.

[0078] It should be noted that in step S3, the first measured data obtained in step S2 is processed to obtain the ordinal component data required for constructing the characteristic curve in step S4.

[0079] S4. Obtain characteristic-related initial data, and construct a characteristic curve set based on the characteristic-related initial data and the sequence component data of all operating conditions. The characteristic curve set includes voltage characteristic-related curves and initial value characteristic-related curves.

[0080] It should be noted that the characteristic-related initial data are obtained according to the corresponding initial values ​​for each operating condition. The characteristic-related initial data includes the initial values ​​of active power, active current, reactive power, and reactive current. In step S4, a characteristic curve set of eight curves, consisting of voltage characteristic correlation curves and initial value characteristic correlation curves, is constructed based on the characteristic-related initial data and the sequence component data for all operating conditions. In this embodiment, the sequence component voltage, sequence component active power, sequence component reactive power, sequence component active current, sequence component reactive current, initial values ​​of active power, initial values ​​of active current, initial values ​​of reactive power, and initial values ​​of reactive current of the new energy unit under all operating conditions are obtained according to steps S3 and S4. The eight curves are as follows: the first voltage characteristic correlation curve is plotted using the sequence component voltage and sequence component active power data for all operating conditions; the second voltage characteristic correlation curve is plotted using the sequence component voltage and sequence component reactive power data for all operating conditions; the third voltage characteristic correlation curve is plotted using the sequence component voltage and sequence component active current data for all operating conditions; the first initial value characteristic correlation curve is plotted using the initial value of reactive current and the sequence component reactive current data for all operating conditions; the second initial value characteristic correlation curve is plotted using the initial value of active current and the sequence component active current data for all operating conditions; the third initial value characteristic correlation curve is plotted using the initial value of active power and the sequence component active power data for all operating conditions; and the fourth initial value characteristic correlation curve is plotted using the initial value of reactive power and the sequence component reactive power data for all operating conditions.

[0081] S5. Obtain the control strategy of the corresponding new energy unit according to the equipment type, and obtain parameter identification data from the characteristic curve set according to the control strategy.

[0082] It should be noted that in step S5, the control test data of the new energy unit is first obtained from the manufacturer of the new energy unit according to the equipment type of the new energy unit, and then the parameter identification data is obtained from the characteristic curve set according to the control strategy.

[0083] S6. The least squares method is used to identify the parameter identification data to obtain the optimal control parameters that match the first measured data.

[0084] It should be noted that in step S6, the least squares method is used to identify the optimal control parameters that match the first measured data based on the parameter identification data obtained in step S5, thereby realizing the new energy unit. In this embodiment, the control parameter identification method of the electromechanical transient model of the new energy unit is applicable to parameter identification simulation of massive operating conditions. This method greatly reduces the workload of model parameter identification, improves the efficiency and accuracy of parameter identification, and makes parameter identification under massive operating condition samples possible.

[0085] This application provides a method for identifying control parameters of an electromechanical transient model for a new energy generating unit. The method includes acquiring the equipment type of the new energy generating unit, and acquiring the operating data required for electromechanical transient operation based on the equipment type. The operating data includes operating conditions and corresponding fault configurations. The fault configurations include active power, reactive power, fault type, and voltage dip / rise parameters. A test system for electromechanical transient operation of the new energy generating unit is constructed based on the equipment type. The test system is controlled to operate according to the operating data to acquire first measured data corresponding to each operating condition. The first measured data includes three-phase voltage and three-phase current. The first measured data is then processed. The method involves obtaining sequence component data corresponding to the duration of voltage drop before and after the fault during the transient process. This sequence component data includes sequence component voltage, sequence component active power, sequence component reactive power, sequence component active current, and sequence component reactive current. Characteristic-related initial data is acquired, and a characteristic curve set is constructed based on this initial data and the sequence component data for all operating conditions. This characteristic curve set includes voltage characteristic-related curves and initial value characteristic-related curves. The control strategy for the corresponding new energy unit is obtained according to the equipment type, and parameter identification data is obtained from the characteristic curve set based on the control strategy. The least squares method is used to identify the parameter identification data, obtaining the optimal control parameters that match the first measured data. This method for identifying control parameters in the electromechanical transient model of the new energy unit enables the identification of control parameters for model simulation, solving the technical problem that existing methods for acquiring or identifying model parameters are labor-intensive and impractical.

[0086] In one embodiment of this application, obtaining parameter identification data from the characteristic curve set according to the control strategy includes:

[0087] The control method for fault ride-through of new energy units during transient operation is determined based on the control strategy.

[0088] Extract voltage characteristic correlation curves and initial value characteristic correlation curves corresponding to the control mode from the characteristic curve set;

[0089] Parameter identification data corresponding to the control mode are obtained from the extracted voltage characteristic correlation curves and initial value characteristic correlation curves;

[0090] The control methods include active power control, active current control, reactive power control, and reactive current control.

[0091] If the control method is active power control, the voltage characteristic correlation curve is the voltage characteristic correlation curve composed of the sequence component voltage and the sequence component active power. The initial value characteristic correlation curve is the initial value of active power composed of the initial value of the characteristic correlation initial data and the sequence component active power. The parameter identification data includes the sequence component active power and the initial value of active power.

[0092] If the control method is active current control, the voltage characteristic correlation curve is the voltage characteristic correlation curve composed of the sequence component voltage and the sequence component active current. The initial value characteristic correlation curve is the initial value characteristic correlation curve composed of the initial value of active current and the sequence component active current. The parameter identification data includes the sequence component active current and the sequence component voltage.

[0093] If the control method is reactive power control, the voltage characteristic correlation curve is the voltage characteristic correlation curve composed of the sequence component voltage and the sequence component reactive power. The initial value characteristic correlation curve is the initial value of reactive power composed of the initial value of the characteristic correlation initial data and the sequence component reactive power. The parameter identification data includes the sequence component reactive power and the initial value of reactive power.

[0094] If the control method is reactive current control, the voltage characteristic correlation curve is the voltage characteristic correlation curve composed of the sequence component voltage and the sequence component reactive current. The initial value characteristic correlation curve is the initial value characteristic correlation curve composed of the initial value of reactive current and the sequence component reactive current. The parameter identification data includes the sequence component reactive current and the sequence component voltage.

[0095] It should be noted that the control strategy is obtained based on the technical data provided by the manufacturer of the new energy unit. For example, taking "fault ride active power control" as an example, if the active power control strategy provided by the manufacturer is "specified active current control," then the active current control mode is selected. This requires plotting a third voltage characteristic correlation curve using the sequence component voltage and sequence component active current data for all operating conditions, and a second initial value characteristic correlation curve using the initial active current and sequence component active current data for all operating conditions to obtain the required parameter identification data. In the third voltage characteristic correlation curve, the amplitude of the sequence component active current is independent of the sequence component voltage and will not change with the amplitude of the sequence component voltage. In this embodiment, after determining the control mode, the characteristic correlation curve is first determined from the characteristic curve set, and then the required data is extracted from the determined characteristic correlation curve to form parameter identification data from the measured data, facilitating the identification of subsequent control parameters.

[0096] In this embodiment of the application, the expression for the active power control method is: P ref =K P *P0+P SET In the formula, P0 is the initial value of active power, P ref For the sequence component active power, K P P SET All of these are control parameters to be identified.

[0097] It should be noted that in the active power control method, the initial value of active power P0 and the sequence component active power P ref The relationship is linear. In the subsequent identification of control parameters, it is necessary to obtain several initial active power values ​​P0 and sequence component active power P from the determined characteristic correlation curves. ref Parameter identification data.

[0098] In this embodiment, the expression for the active current control method is: IP ref =K V *V t +K I *IP0+IP SET In the formula, IP0 is the initial value of the active current, and IP ref For the sequence component of active current, K V IP SET All of these are control parameters to be identified.

[0099] It should be noted that in the active current control method, the sequence component voltage V t With sequence component active current IP ref The relationship is linear. In the subsequent identification of control parameters, it is necessary to obtain the required number of sequence component voltages V from the determined characteristic correlation curve. tWith sequence component active current IP ref The parameter identification data. In this embodiment, it varies with different voltage drops under different operating conditions; K I To superimpose the coefficient of the initial active current IP0, generally under the control mode of "superimposing the initial active current", K I =1, under other control methods, K I =0.

[0100] In this embodiment, the expression for the reactive power control method is: Q ref =K Q *Q0+Q SET In the formula, Q0 is the initial value of reactive power, Q ref K represents the sequence component reactive power. Q Q SET All of these are control parameters to be identified.

[0101] It should be noted that in reactive power control, the initial reactive power value Q0 and the sequence component reactive power Q are different. ref The relationship is linear. In the subsequent identification of control parameters, it is necessary to obtain several initial reactive power values ​​Q0 and sequence component reactive power Q from the determined characteristic correlation curves. ref Parameter identification data.

[0102] In this embodiment, the expression for the reactive current control method is: IQ ref =K Q *(V SET -V t )+K Q *IQ0+IQ SET In the formula, IQ0 is the initial value of reactive current, and IQ ref K represents the sequence component of the reactive current. Q IQ SET All of these are control parameters to be identified.

[0103] It should be noted that in the reactive current control method, the sequence component voltage V t The sequence component of reactive current IQ ref The relationship is linear. In the subsequent identification of control parameters, it is necessary to obtain the required number of sequence component voltages V from the determined characteristic correlation curve. t The sequence component of reactive current IQ ref The parameter identification data. In this embodiment, it varies with different voltage drops under different operating conditions; K Q To superimpose the coefficient of the initial reactive current IQ0, generally under the control mode of "superimposing the initial reactive current", K Q =1, under other control methods, K Q=0. The coefficient is provided by the manufacturer's control strategy, such as: V SET This is the fault ride-through voltage threshold setting, typically set to V. SET Set it to 0.9.

[0104] In one embodiment of this application, the least squares method is used to identify the parameter identification data, and the optimal control parameters that match the first measured data are as follows:

[0105] The least squares method was used to fit the parameter identification data to obtain a linear regression equation;

[0106] The predicted value is obtained based on the linear regression equation. The linear regression equation is optimized with the goal of minimizing the mean square error between the predicted value and the corresponding data in the first measured data, and the optimal linear regression equation is obtained.

[0107] The optimal control parameters that match the first measured data are obtained from the optimal linear regression equation.

[0108] It should be noted that, in the xy-axis coordinate system, the initial value of active power P0 and the sequence component voltage V are identified based on the acquired parameter data. t Alternatively, the initial reactive power value Q0 can be used as the x-axis value, and the sequence component active power P can be... ref Sequence component active current IP ref Sequence component reactive power Q ref or sequence component reactive current IQ ref A scatter plot is constructed in the coordinate system using the y-axis values. The least squares method is applied to fit all points from the parameter identification data in the scatter plot, resulting in a linear regression equation in the form of a linear function y = a + bx, where a and b represent control parameters. Specifically, the linear regression equation is optimized with the objective of minimizing the mean square error between the predicted y-value of the linear function and the corresponding active power, reactive power, active current, or reactive current obtained from the first measured data. The optimal linear regression equation is then used as the optimal linear regression equation, and the values ​​of a and b in the optimal linear regression equation are used as the optimal control parameters. The specific implementation of the least squares method is a relatively mature technology in this field, and its details will not be elaborated here.

[0109] In one embodiment of this application, the first measured data is processed to obtain sequence component data corresponding to the duration of voltage drop before and after the fault during the transient process, including:

[0110] The first measured data is filtered, denoised, and outlier removed to obtain the processed data;

[0111] The second measured data is obtained by extracting data on the duration of voltage drop before and after the fault during the transient process from the processed data.

[0112] The second measured data was processed using Fast Fourier Transform and Positive / Negative Sequence Transform to obtain sequence component data corresponding to the duration of voltage drop before and after the fault during the transient process.

[0113] It should be noted that, firstly, the first measured data, consisting of three-phase voltage and three-phase current, is processed by filtering, noise reduction, and outlier removal to obtain processed data, which improves data quality. Simultaneously, the processing is verified to ensure data integrity and accuracy. Then, the time periods and data segments related to the electromechanical transient process are extracted from the processed data, namely, data from 1 second before the fault, the duration of the voltage drop, and 5 seconds after the fault, as the second measured data. By performing Fast Fourier Transform (e.g., FFT algorithm) and positive / negative sequence transform on the three-phase voltage and three-phase current of the second measured data, the sequence component voltage V, including both positive and negative sequences, is calculated. t The sequence component active power, sequence component reactive power, sequence component active current, and sequence component reactive current are calculated, and the numerical results of the sequence component voltage, sequence component active power, sequence component reactive power, sequence component active current, and sequence component reactive current during the fault period are calculated in sequence. The sequence component voltage V, including both positive and negative sequence components, is also calculated. t The methods for sequence component active power, sequence component reactive power, sequence component active current, and sequence component reactive current are conventional methods for power system analysis and calculation, and will not be elaborated further in this embodiment.

[0114] Example 2:

[0115] Figure 2 This is a flowchart illustrating the steps of the verification method for the electromechanical transient model of the new energy unit described in this application embodiment.

[0116] like Figure 2 As shown in the figure, this application provides a method for verifying the electromechanical transient model of a new energy unit, including the following steps:

[0117] S10. Obtain the electromechanical transient model and equipment type of the new energy unit;

[0118] S20. Obtain the operating data required for electromechanical transient analysis based on the equipment type, and obtain the optimal control parameters and first measured data corresponding to the operating data using the control parameter identification method of the electromechanical transient model of the above-mentioned new energy unit based on the equipment type;

[0119] S30. Simulate the electromechanical transient model based on the operating data and optimal control parameters to obtain simulation data;

[0120] S40. Compare the first measured data with the simulation data to obtain the comparison results; determine whether the electromechanical transient model meets the model standard based on the comparison results.

[0121] It should be noted that the method for identifying control parameters of the electromechanical transient model of the new energy unit in this embodiment has already been described in Embodiment 1, and will not be repeated in this embodiment. In this embodiment, after obtaining the optimal control parameters and the first measured data corresponding to the operating data from the method for identifying control parameters of the electromechanical transient model of the new energy unit, the constructed electromechanical transient model is run and simulation data of the same quantity as the first measured data is generated. The simulation data includes positive sequence voltage, positive sequence active power, positive sequence reactive power, positive sequence active current, and positive sequence reactive current. The verification method for the electromechanical transient model of this new energy unit compares the simulation data with the first measured data (such as active power, reactive power, active current and reactive current), and calculates the model error deviation according to the standard requirements to obtain the comparison results (such as the error between positive sequence active power, the error between positive sequence reactive power, the error between positive sequence reactive current and reactive current, and the error between positive sequence active current). The comparison results are used to measure whether the established electromechanical transient model meets the model standard. For example: if the object of the electromechanical transient model is a wind turbine, the calculation error shall be calculated according to the standard "NB / T 31053-2021 Verification Procedure for Electrical Simulation Model of Wind Turbine"; if the object of the electromechanical transient model is the dynamic reactive power compensation SVG of a wind farm, the calculation error shall be calculated according to the standard "NB / T 10316-2019 Test Specification for Grid Connection Performance of Dynamic Reactive Power Compensation Device of Wind Farm"; if the object of the electromechanical transient model is a photovoltaic inverter, the calculation error shall be calculated according to the standard "GB / T 32892-2016 Test Procedure for Model and Parameters of Photovoltaic Power Generation System"; if the object of the electromechanical transient model is the dynamic reactive power compensation SVG of a photovoltaic power station, the calculation error shall be calculated according to the standard "Technical Specification for Testing Reactive Power Compensation Device of Photovoltaic Power Station" GB / T 34931-2017. Among them, the following standards are existing technologies in this field: "NB / T31053-2021 Verification Procedure for Electrical Simulation Model of Wind Turbine Units", "NB / T 10316-2019 Grid-connected Performance Test Specification for Dynamic Reactive Power Compensation Devices in Wind Farms", "GB / T 32892-2016 Test Procedure for Models and Parameters of Photovoltaic Power Generation Systems", and "Technical Specification for Testing Reactive Power Compensation Devices in Photovoltaic Power Stations". Their contents will not be elaborated upon here. Model standards can be obtained from these standards.

[0122] In this embodiment, the verification method for the electromechanical transient model of the new energy unit verifies the accuracy of the electromechanical transient model under various operating conditions by identifying the optimal control parameters of the control parameter identification method for the electromechanical transient model of the new energy unit. These conditions include different fault types, fault locations, fault durations, and different operating conditions, ensuring that the accuracy of the electromechanical transient model under various operating conditions is fully verified, thus improving the reliability and safety of the electromechanical transient model. This verification method for the electromechanical transient model of the new energy unit ensures the comparability and reliability of the verification results, improving the efficiency and accuracy of model building.

[0123] It should be noted that the verification method for the electromechanical transient model of this new energy unit and the method for identifying the control parameters of the electromechanical transient model can be applied to different types of new energy units, such as wind power, solar power, and electrochemical energy storage, as well as to different dynamic reactive power compensation equipment, demonstrating broad application prospects. The verification method for the electromechanical transient model and the method for identifying the control parameters of the electromechanical transient model improve the accuracy and reliability of the electromechanical transient model, reduce the workload of parameter identification, further reduce the operation and maintenance costs of the new energy unit, and improve economic efficiency.

[0124] In this embodiment of the application, the verification method for the electromechanical transient model of the new energy unit further includes generating a model report based on the comparison results and model standards.

[0125] It should be noted that the generated model report includes the following steps: First, determine the report structure, including the chapter structure of the planning report, covering sections such as introduction, model overview, validation metrics, results analysis, conclusions, and recommendations. Then, use the template function of a dedicated document template tool (such as Microsoft Word) to generate a formatted report. Second, perform report data mapping and filling, that is, fill the collected model parameters, training data features, validation results, and other data into the corresponding placeholder positions according to the format and structure set in the report template. For example, fill the model accuracy value into the "Validation Metrics - Accuracy" position in the report template. Finally, write an automated script to call the template tool and fill in the data, generating a complete model validation report with one click. For example, running the script can generate the report, supporting multiple formats such as PDF, Word, and HTML, facilitating reading and sharing. In this embodiment, the introduction explains the purpose and background of the report; the model overview introduces the model type and parameters; the validation metrics list various evaluation metrics and their values; the results analysis interprets the meaning of the metrics and their achievement status; and the conclusions and recommendations summarize the model performance and propose directions for improvement.

[0126] Example 3:

[0127] Figure 3 This is a schematic diagram of the control parameter identification device for the electromechanical transient model of the new energy unit described in this application embodiment.

[0128] like Figure 3 As shown in the figure, this application embodiment provides a control parameter identification device for the electromechanical transient model of a new energy unit, including a first data acquisition module 10, a second data acquisition module 20, a data processing module 30, a curve construction module 40, a data extraction module 50, and a parameter identification module 60;

[0129] The first data acquisition module 10 is used to acquire the equipment type of the new energy unit and to acquire the operating data required for electromechanical transients based on the equipment type. The operating data includes operating conditions and fault configurations corresponding to the operating conditions. The fault configurations include active power, reactive power, fault type and voltage drop / rise series parameters.

[0130] The second data acquisition module 20 is used to construct a test system for electromechanical transient operation of the new energy unit according to the equipment type, control the operation of the test system according to the operation data, and acquire first test data for electromechanical transient operation of the new energy unit corresponding to each of the operating conditions. The first test data includes three-phase voltage and three-phase current.

[0131] Data processing module 30 is used to process the first measured data to obtain sequence component data corresponding to the duration of voltage drop before the fault and after the fault during the transient process. The sequence component data includes sequence component voltage, sequence component active power, sequence component reactive power, sequence component active current and sequence component reactive current.

[0132] The curve construction module 40 is used to obtain characteristic-related initial data and construct a characteristic curve set based on the characteristic-related initial data and the sequence component data of all operating conditions. The characteristic curve set includes voltage characteristic-related curves and initial value characteristic-related curves.

[0133] The data extraction module 50 is used to obtain the control strategy of the corresponding new energy unit according to the equipment type, and to obtain parameter identification data from the characteristic curve set according to the control strategy;

[0134] The parameter identification module 60 is used to identify the parameter identification data using the least squares method to obtain the optimal control parameters that match the first measured data.

[0135] It should be noted that the content of the modules in the apparatus of Embodiment 3 has already been described in the steps of the method of Embodiment 1, and the content of the control parameter identification device module of the electromechanical transient model of the new energy unit will not be described again in this embodiment. In this embodiment, the control parameter identification device of the electromechanical transient model of the new energy unit realizes the identification of the control parameters of the new energy unit through a first data acquisition module, a second data acquisition module, a data processing module, a curve construction module, a data extraction module, and a parameter identification module.

[0136] In this embodiment, the parameter identification module 60 is further configured to fit the parameter identification data using the least squares method to obtain a linear regression equation; obtain a predicted value based on the linear regression equation; optimize the linear regression equation with the goal of minimizing the mean square error between the predicted value and the corresponding data in the first measured data to obtain the optimal linear regression equation; and obtain the optimal control parameters that match the first measured data from the optimal linear regression equation.

[0137] Example 4:

[0138] Figure 4 This is a schematic diagram of the terminal device described in an embodiment of this application.

[0139] like Figure 4 As shown, this application provides a terminal device, including a processor and a memory;

[0140] Memory is used to store program code and transfer the program code to the processor;

[0141] The processor is used to execute the control parameter identification method of the electromechanical transient model of the new energy unit according to the instructions in the program code.

[0142] It should be noted that the processor is used to execute the steps in the above-described embodiment of the control parameter identification method for an electromechanical transient model of a new energy unit according to the instructions in the program code. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described system / device embodiments.

[0143] For example, a computer program can be divided into one or more modules / units, one or more of which are stored in memory and executed by a processor to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a terminal device.

[0144] Terminal devices can be computing devices such as desktop computers, laptops, handheld computers, and cloud servers. Terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will understand that this does not constitute a limitation on the terminal device, which may include more or fewer components than illustrated, or combinations of certain components, or different components. For example, a terminal device may also include input / output devices, network access devices, buses, etc.

[0145] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0146] Memory can be an internal storage unit of a terminal device, such as a hard drive or RAM. Memory can also be an external storage device, such as a plug-in hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, memory can include both internal and external storage units. Memory is used to store computer programs and other programs and data required by the terminal device. Memory can also be used to temporarily store data that has been output or will be output.

[0147] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0149] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0150] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0151] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0152] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for identifying control parameters of an electromechanical transient model of a new energy unit, characterized in that, Includes the following steps: The equipment type of the new energy unit is obtained, and the operating data required for electromechanical transients is obtained according to the equipment type. The operating data includes the operating conditions and the fault configurations corresponding to the operating conditions. A test system for electromechanical transient operation of the new energy unit is constructed according to the equipment type. The operation of the test system is controlled according to the operation data to obtain the first test data for electromechanical transient operation of the new energy unit corresponding to each of the operating conditions. The first measured data is processed to obtain sequence component data corresponding to the duration of voltage drop before and after the fault during the transient process; Obtain characteristic-related initial data, and construct a characteristic curve set based on the characteristic-related initial data and the sequence component data of all the operating conditions; According to the equipment type, obtain the control strategy corresponding to the new energy unit, and according to the control strategy, obtain parameter identification data from the characteristic curve set; The least squares method is used to identify the parameter identification data to obtain the optimal control parameters that match the first measured data; The characteristic curve set includes voltage characteristic correlation curves and initial value characteristic correlation curves. Obtaining parameter identification data from the characteristic curve set according to the control strategy includes: The control method for fault ride-through of the new energy unit during transient operation is determined based on the control strategy. Extract the voltage characteristic correlation curve and the initial value characteristic correlation curve corresponding to the control method from the characteristic curve set; Parameter identification data corresponding to the control mode are obtained from the extracted voltage characteristic correlation curve and the initial value characteristic correlation curve.

2. The method for identifying control parameters of the electromechanical transient model of a new energy unit according to claim 1, characterized in that, The sequence component data includes sequence component voltage, sequence component active power, sequence component reactive power, sequence component active current, and sequence component reactive current. The control methods include active power control, active current control, reactive power control, and reactive current control. If the control method is active power control, then the voltage characteristic correlation curve is the voltage characteristic correlation curve composed of the sequence component voltage and the sequence component active power, the initial value characteristic correlation curve is the initial value of active power composed of the initial value of the characteristic correlation initial data and the sequence component active power, and the parameter identification data includes the sequence component active power and the initial value of active power. If the control method is active current control, then the voltage characteristic correlation curve is the voltage characteristic correlation curve composed of the sequence component voltage and the sequence component active current, the initial value characteristic correlation curve is the initial value of the active current of the characteristic correlation initial data and the sequence component active current, and the parameter identification data includes the sequence component active current and the sequence component voltage. If the control method is a reactive power control method, then the voltage characteristic correlation curve is a voltage characteristic correlation curve composed of the sequence component voltage and the sequence component reactive power, the initial value characteristic correlation curve is an initial value characteristic correlation curve composed of the initial reactive power value of the characteristic correlation initial data and the sequence component reactive power, and the parameter identification data includes the sequence component reactive power and the initial reactive power value. If the control method is reactive current control, then the voltage characteristic correlation curve is the voltage characteristic correlation curve composed of the sequence component voltage and the sequence component reactive current, the initial value characteristic correlation curve is the initial value of the reactive current of the characteristic correlation initial data and the sequence component reactive current, and the parameter identification data includes the sequence component reactive current and the sequence component voltage.

3. The method for identifying control parameters of the electromechanical transient model of a new energy unit according to claim 1, characterized in that, The parameter identification data is identified using the least squares method to obtain the optimal control parameters that match the first measured data, including: The parameter identification data were fitted using the least squares method to obtain a linear regression equation; The predicted value is obtained based on the linear regression equation. The linear regression equation is optimized with the goal of minimizing the mean square error between the predicted value and the corresponding data in the first measured data, so as to obtain the optimal linear regression equation. The optimal control parameters that match the first measured data are obtained from the optimal linear regression equation.

4. The method for identifying control parameters of the electromechanical transient model of a new energy unit according to any one of claims 1-3, characterized in that, The test system includes a controller, an input / output connection board, and a power grid simulator corresponding to the device type. The controller is connected to the power grid simulator through the input / output connection board.

5. The method for identifying control parameters of the electromechanical transient model of a new energy unit according to any one of claims 1-3, characterized in that, The first measured data is processed to obtain sequence component data corresponding to the duration of voltage drop before and after the fault during the transient process, including: The first measured data is filtered, denoised, and outlier removed to obtain processed data. The second measured data is obtained by acquiring data on the duration of voltage drop before and after the fault during the transient process from the processed data. The second measured data is processed using Fast Fourier Transform and Positive / Negative Sequence Transform to obtain sequence component data corresponding to the duration of voltage drop before and after the fault during the transient process.

6. A method for verifying the electromechanical transient model of a new energy unit, characterized in that, Includes the following steps: Obtain the electromechanical transient model and equipment type of the new energy unit; According to the equipment type, obtain the operating data required for electromechanical transients, and according to the equipment type, use the control parameter identification method of the electromechanical transient model of the new energy unit as described in any one of claims 1-5 to obtain the optimal control parameters and the first measured data corresponding to the operating data; The electromechanical transient model is simulated based on the operating data and the optimal control parameters to obtain simulation data. The first measured data is compared with the simulation data to obtain a comparison result; based on the comparison result, it is determined whether the electromechanical transient model meets the model standard.

7. A control parameter identification device for the electromechanical transient model of a new energy unit, characterized in that, include: The system comprises a first data acquisition module, a second data acquisition module, a data processing module, a curve construction module, a data extraction module, and a parameter identification module. The first data acquisition module is used to acquire the equipment type of the new energy unit, and to acquire the operating data required for electromechanical transients according to the equipment type. The operating data includes operating conditions and fault configurations corresponding to the operating conditions. The second data acquisition module is used to construct a test system for electromechanical transient operation of the new energy unit according to the equipment type, control the operation of the test system according to the operation data, and acquire the first test data for electromechanical transient operation of the new energy unit corresponding to each of the operating conditions. The data processing module is used to process the first measured data to obtain sequence component data corresponding to the duration of voltage drop before and after the fault during the transient process. The curve construction module is used to obtain characteristic-related initial data and construct a set of characteristic curves based on the characteristic-related initial data and the sequence component data of all the operating conditions. The data extraction module is used to obtain the control strategy corresponding to the new energy unit according to the equipment type, and to obtain parameter identification data from the characteristic curve set according to the control strategy; The parameter identification module is used to identify the parameter identification data using the least squares method to obtain the optimal control parameters that match the first measured data. The characteristic curve set includes voltage characteristic correlation curves and initial value characteristic correlation curves. Obtaining parameter identification data from the characteristic curve set according to the control strategy includes: The control method for fault ride-through of the new energy unit during transient operation is determined based on the control strategy. Extract the voltage characteristic correlation curve and the initial value characteristic correlation curve corresponding to the control method from the characteristic curve set; Parameter identification data corresponding to the control mode are obtained from the extracted voltage characteristic correlation curve and the initial value characteristic correlation curve.

8. The control parameter identification device for the electromechanical transient model of a new energy unit according to claim 7, characterized in that, The parameter identification module is further configured to fit the parameter identification data using the least squares method to obtain a linear regression equation; obtain a predicted value based on the linear regression equation; optimize the linear regression equation with the goal of minimizing the mean square error between the predicted value and the corresponding data in the first measured data to obtain an optimal linear regression equation; and obtain the optimal control parameters that match the first measured data from the optimal linear regression equation.

9. A terminal device, characterized in that, Including the processor and memory; The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the control parameter identification method for the electromechanical transient model of the new energy unit as described in any one of claims 1-5, according to the instructions in the program code.

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