Electromechanical transient simulation model checking method, equipment and product

Abstract

The invention discloses an electromechanical transient simulation model checking method, equipment and a product, and relates to the field of power system simulation model checking, and the method comprises the steps: carrying out the data screening of the obtained actual measurement data of target electromechanical equipment through employing an inflection point algorithm, obtaining the actual measurement screening data, determining a fault starting moment and a fault ending moment, and carrying out the verification of the fault starting moment and the fault ending moment; marking the actually-measured screening data by using the fault starting moment and the fault ending moment to obtain actually-measured marked data; and checking the obtained simulation data of the electromechanical transient simulation model and the obtained actual measurement mark data to obtain a check file. According to the method, the checking precision of the electromechanical transient simulation model can be improved while the checking working efficiency of the electromechanical transient simulation model is improved.

Description

Technical Field

[0001] This application relates to the field of power system simulation model verification technology, and in particular to a method, equipment and product for verifying electromechanical transient simulation models. Background Technology

[0002] With the continuous increase in the proportion of new energy power generation, the large-scale grid connection of new energy has increased the uncertainty of grid operation. As a core technical means to study the safety and stability of the power grid, the establishment of electromechanical transient simulation models is widely used in power system simulation analysis.

[0003] Establishing electromechanical transient simulation models for new energy power plants is a fundamental step in power grid simulation analysis, and the accuracy of these models directly affects the conclusions of power grid system stability analysis. Verification of electromechanical transient simulation models, serving as a bridge between modeling theory and engineering applications, is a crucial step in validating the model's effectiveness. However, the verification of electromechanical transient simulation models suffers from problems such as insufficient verification accuracy, low verification efficiency, and high reliance on manual verification. Summary of the Invention

[0004] This invention proposes a method, equipment, and product for verifying electromechanical transient simulation models, which can improve the efficiency and accuracy of electromechanical transient simulation model verification.

[0005] To achieve the above objectives, this application provides the following solution:

[0006] Firstly, this application provides a method for verifying an electromechanical transient simulation model, including:

[0007] Obtain measured data of the target electromechanical equipment.

[0008] The measured data is filtered using an inflection point algorithm to obtain filtered measured data, and the fault start time and fault end time of the filtered measured data are determined.

[0009] The measured filtered data are marked using the fault start time and the fault end time to obtain measured marked data.

[0010] The simulation data of the electromechanical transient simulation model is obtained; the simulation data is based on the simulation software, the electromechanical transient simulation model corresponding to the target electromechanical equipment, the measured data, and the data determined by the fault start time.

[0011] The simulation data is compared with the measured marked data to obtain a verification file.

[0012] Secondly, this application provides a computer device, including: data processing software and simulation software.

[0013] The data processing software is used to implement the electromechanical transient simulation model verification method described in any of the above-mentioned methods.

[0014] The simulation software is used to acquire the electromechanical transient simulation model, measured data, and fault start time corresponding to the target electromechanical equipment, and to determine the simulation data based on the electromechanical transient simulation model, measured data, and fault start time corresponding to the target electromechanical equipment.

[0015] Optionally, it also includes a display module and a storage module; the display module is used to display the verification file; the storage module is used to store the verification file.

[0016] Thirdly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the electromechanical transient simulation model verification method described in any one of the above.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects:

[0018] This invention proposes a method, device, and product for verifying electromechanical transient simulation models. By utilizing an inflection point algorithm to filter measured data of the target electromechanical equipment, the amount of data to be verified is reduced, and the speed of subsequent data processing is improved. The invention also determines the fault start and end times, marking the filtered measured data using these times. Simulation software is then used to process the corresponding electromechanical transient simulation model of the target electromechanical equipment based on the filtered data and the fault start time to obtain simulation data. Finally, the marked data and simulation data are verified. This application solves the technical problems of low efficiency and high workload associated with the existing method of manually marking data images. It can automatically complete the processing of measured data of electromechanical equipment, the processing of the electromechanical transient simulation model to obtain corresponding simulation data, and the comparison and verification of simulation data and measured data to obtain verification files, thus improving the efficiency of electromechanical transient simulation model verification. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments 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.

[0020] Figure 1 This is a diagram illustrating the application environment of an electromechanical transient simulation model verification method according to an embodiment of this application.

[0021] Figure 2 This is a flowchart of a method for verifying an electromechanical transient simulation model according to an embodiment of this application;

[0022] Figure 3 This is a schematic diagram of data marking for a method for verifying an electromechanical transient simulation model provided in an embodiment of this application;

[0023] Figure 4 for Figure 2 Detailed flowchart of step 400;

[0024] Figure 5 Verification diagram provided for another embodiment of this application; Figure 5 In the image, (a) is the fundamental positive sequence voltage data map, (b) is the active current data map, (c) is the reactive current data map, (d) is the active power data map, and (e) is the reactive power data map.

[0025] Figure 6 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application;

[0026] Figure 7 This is a system block diagram of a Python-based electromechanical transient model verification system for new energy generating units, provided as an embodiment of this application.

[0027] Attached image labels: 102 terminal, 104 server. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] The electromechanical transient simulation model verification method provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be set up independently, integrated into server 104, or placed in the cloud or on another server. Terminal 102 can send measured data of the target electromechanical equipment to server 104. Server 104 receives the measured data of the target electromechanical equipment and uses an inflection point algorithm to filter the measured data, obtaining filtered measured data, and determines the fault start time and fault end time of the filtered measured data; it then marks the filtered measured data using the fault start time and fault end time, obtaining marked measured data; it acquires simulation data from the electromechanical transient simulation model; the simulation data is based on simulation software, the electromechanical transient simulation model corresponding to the target electromechanical equipment, the measured data, and the fault start time; the simulation data is then verified against the marked measured data to obtain a verification file. Server 104 can feed back the obtained verification file to terminal 102. In addition, in some embodiments, the electromechanical transient simulation model verification method can also be implemented by the server 104 or the terminal 102 separately. For example, the terminal 102 can directly process the measured data of the target electromechanical equipment, or the server 104 can obtain the measured data of the target electromechanical equipment from the data storage system and perform data processing on the measured data of the target electromechanical equipment.

[0031] The terminal 102 can be, but is not limited to, various desktop computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle devices. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster composed of multiple servers, or it can be a cloud server.

[0032] In one exemplary embodiment, such as Figure 2 As shown, a method for verifying an electromechanical transient simulation model is provided. This method is executed by a computer device, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is applied to... Figure 1 Taking server 104 as an example, the explanation includes steps 100 to 500. Wherein:

[0033] Step 100: Obtain the measured data of the target electromechanical equipment.

[0034] Step 200: Use the inflection point algorithm to filter the measured data to obtain the filtered measured data, and determine the fault start time and fault end time of the filtered measured data.

[0035] Step 300: Mark the measured screening data using the fault start time and the fault end time to obtain measured marked data.

[0036] Step 400: Obtain simulation data of the electromechanical transient simulation model; the simulation data is based on the simulation software, the electromechanical transient simulation model corresponding to the target electromechanical equipment, the measured data, and the data determined by the fault start time.

[0037] Step 500: Verify the simulation data with the measured marker data to obtain a verification file.

[0038] In an exemplary embodiment, step 200 involves using an inflection point algorithm to filter the measured data to obtain filtered measured data, specifically including:

[0039] The measured data are preprocessed using the Fourier algorithm to obtain the fundamental positive sequence component data; the measured data are instantaneous data of phase voltage and phase current; the fundamental positive sequence component data includes voltage, active power, reactive power, active current and reactive current.

[0040] The voltage data in the fundamental positive sequence component data is filtered using the inflection point detection function of the ruptures package to obtain the measured filtered data; wherein, the loss function model of the inflection point detection function is set to l2, and the number of inflection points n_bkps is set to 2.

[0041] The measured screening data is the data from 2 seconds before the fault to 2 seconds after the fault.

[0042] The inflection point detection function can be `ruptures.Binseg()`, which is suitable for large-scale data filtering. Binseg is an inflection point detection algorithm based on binary segmentation, where the loss function model is set to l2 and the number of inflection points n_bkps is set to 2. The principle of change point detection: The core idea of ​​the `ruptures` library is to find the change point location that minimizes the piecewise approximation error through an optimization algorithm.

[0043] In an exemplary embodiment, step 200, which involves determining the fault start time and fault end time of the measured screening data, is described in detail, specifically including:

[0044] The `where` function from the NumPy package is used to determine the fault start and end times of the measured filtered data. The condition for the `where` function is set as follows: the input data is less than a first preset parameter, or the input data is greater than a second preset parameter. Specifically, when the measured data corresponds to a voltage drop fault, the condition for the `where` function is set as follows: the input data is less than the first preset parameter; when the measured data corresponds to a voltage rise fault, the condition for the `where` function is set as follows: the input data is greater than the second preset parameter.

[0045] The implementation principle of the WHERE function is as follows: (1) Check the validity of the input parameters, that is, the input conditions must be a Boolean array or a conditional expression; (2) Create an empty array with the same shape and type as the input array according to the shape and data type of the input array; (3) Traverse the input array and judge whether each element meets the conditions according to the conditions; (4) Return the result, that is, the fault start time and the fault end time.

[0046] In an exemplary embodiment, the measured labeled data in step 300 is divided into five intervals: the pre-fault interval, the transient interval during the fault, the steady-state interval during the fault, the post-fault transient interval, and the post-fault steady-state interval.

[0047] Step 300, which involves marking the measured screening data using the fault start time and the fault end time to obtain measured marked data, specifically includes:

[0048] Step 301: Obtain the steady-state start time after the fault and the steady-state start time after the fault is cleared; the steady-state start time after the fault is the time corresponding to the first time after a first preset time delay, where the first time is the time when the fluctuation of the first specified data enters the first specified range; the steady-state start time after the fault is cleared is the time corresponding to the second time after a second preset time delay, where the second time is the time when the fluctuation of the second specified data enters the second specified range; the steady-state start time after the fault is between the fault start time and the fault end time, and the steady-state start time after the fault is cleared is after the fault end time.

[0049] Step 302: Mark the data interval from the third preset time before the fault start time to the fault start time as the pre-fault interval; mark the data interval from the fault start time to the post-fault steady-state start time as the transient interval during the fault; mark the data interval from the post-fault steady-state start time to the fault end time as the steady-state interval during the fault; mark the data interval from the fault end time to the post-fault clearing steady-state start time as the post-fault transient interval; mark the data interval from the post-fault clearing steady-state start time to the fourth preset time after the post-fault clearing steady-state start time as the post-fault steady-state interval.

[0050] Furthermore, such as Figure 3 As shown, in step 301, the first specified data and the second specified data are both reactive current, the first specified range and the second specified range are both ±10% of the rated value range, the first preset time and the second preset time are both 20ms, and the third preset time and the fourth preset time are both 1s. The steady-state start time tfaultQS after the fault and the steady-state start time tclearQS after the fault are determined according to the 20ms after the moment when the reactive current fluctuation begins to enter the ±10% rated value range. The interval marking of the data is achieved by pushing tfault forward by 1 second to tbegin and tclearQS backward by 1 second to tend.

[0051] In step 302, the interval from tbegin to the fault start time tfault is the pre-fault interval; the interval from the fault start time tfaul to the steady-state start time tfaultQS after the fault is the transient interval during the fault period; the interval from the steady-state start time tfaultQS after the fault to the fault end time tclear is the steady-state interval during the fault period; the interval from the fault end time tclear to the steady-state start time tclearQS after the fault is cleared is the transient interval after the fault; and the interval from the steady-state start time tclearQS after the fault is cleared to tend is the steady-state interval after the fault, thus obtaining the measured marked data.

[0052] In an exemplary embodiment, the measured marked data in step 300 can also be divided into three intervals: the pre-fault interval, the fault period interval, and the post-fault interval. The difference from the previous embodiment is that it is not necessary to confirm the steady-state start time tfaultQS after the fault and the steady-state start time tclearQS after the fault is cleared. It is not necessary to mark the data during the fault period and after the fault in intervals. The timetend is determined by delaying the fault end time by a preset time.

[0053] In one exemplary embodiment, the process of determining the simulation data involved in step 400 is described in detail, specifically including:

[0054] Determine the power flow file and stability file of the electromechanical transient simulation model corresponding to the target electromechanical equipment.

[0055] Using the open file read / write function in Python, the voltage value, maximum active power output, actual active power output, and scheduled reactive power output are loaded into the power flow file based on the measured data, resulting in an updated power flow file.

[0056] Using the open file read / write function in Python, the fault start time and fault impedance X value are loaded into the stability file to obtain the updated stability file.

[0057] The simulation data of the electromechanical transient simulation model is obtained by running the updated power flow file and stability file using simulation software.

[0058] In an exemplary embodiment, step 400 involves running the updated power flow file and stability file using simulation software to obtain simulation data for the electromechanical transient simulation model, specifically including:

[0059] S1. Set the initial value of the fault impedance X of the electromechanical transient simulation model and determine the fault type based on the measured data; wherein, Xmax≥X≥Xmin, the initial value of Xmax is the maximum fault impedance supported by the electromechanical transient simulation model, and the initial value of Xmin is the minimum fault impedance supported by the electromechanical transient simulation model.

[0060] S2. The average effective value of the measured screening data during the fault period is determined as the first voltage.

[0061] S3. Run the updated power flow file and stability file corresponding to the current stage through simulation software to obtain the simulation data of the current stage corresponding to the electromechanical transient simulation model, and determine the average effective value of the voltage of the current stage simulation data during the fault as the second voltage.

[0062] S4. Determine whether the difference between the first voltage and the second voltage is less than a preset threshold to obtain a first determination result.

[0063] S5. When the first judgment result indicates that the difference between the first voltage and the second voltage is less than a preset threshold, execute S9.

[0064] S6. When the first judgment result indicates that the difference between the first voltage and the second voltage is not less than the preset threshold, and the fault type is voltage drop, execute S7; when the first judgment result indicates that the difference between the first voltage and the second voltage is not less than the preset threshold, and the fault type is voltage rise, execute S8.

[0065] S7. When the first voltage is greater than the second voltage, update Xmax to the fault impedance X, update the fault impedance X corresponding to the current stage according to the first formula to obtain the updated fault impedance X, update the updated power flow file and stability file corresponding to the current stage based on the updated fault impedance X, and return to S3; when the first voltage is not greater than the second voltage, update Xmin to the fault impedance X, update the fault impedance X corresponding to the current stage according to the first formula to obtain the updated fault impedance X, update the updated power flow file and stability file corresponding to the current stage based on the updated fault impedance X, and return to S3.

[0066] The first formula is as follows:

[0067]

[0068] Where X is the updated fault impedance; when the current Xmax1 is the updated Xmax, Xmin1 is Xmin; when Xmax1 is Xmax, Xmin1 is the updated Xmin.

[0069] S8. When the first voltage is greater than the second voltage, update Xmin to the fault impedance X, update the fault impedance X corresponding to the current stage according to the first formula to obtain the updated fault impedance X, update the updated power flow file and stability file corresponding to the current stage based on the updated fault impedance X, and return to S3; when the first voltage is not greater than the second voltage, update Xmax to the fault impedance X, update the fault impedance X corresponding to the current stage according to the first formula to obtain the updated fault impedance X, update the updated power flow file and stability file corresponding to the current stage based on the updated fault impedance X, and return to S3.

[0070] S9. Determine the simulation data of the current stage as the final simulation data.

[0071] For the operation and processing flow of the above embodiments, reference can also be made to Figure 4 .

[0072] In another exemplary embodiment of the present application, taking a 6.25MW wind turbine converter in a certain wind farm as an example, the test conditions are voltage dip to 35% Un, three-phase dip, and 10% Pn < P < 30% Pn, including:

[0073] Step 1: Read the mat file data of the measured instantaneous phase voltage and phase current on the high-voltage side (it can also be to read the instantaneous data of phase voltage and phase current in different formats, including on-site test data or semi-physical hardware-in-the-loop simulation test data).

[0074] Step 2: Use the Fourier algorithm to calculate and obtain the fundamental positive sequence components, including voltage, active power, reactive power, active current, and reactive current.

[0075] Based on the calculated measured fundamental positive sequence voltage data, use the inflection point detection function Binseg of the ruptures package to screen the verification data from 2 seconds before the fault to the fault for 2 seconds as the measured screening data, including the intervals before the fault, during the fault, and after the fault. Among them, the loss function model model is set to l2, and the number of inflection points n_bkps is set to 2.

[0076] The fault start time tfault and fault end time tclear are determined using the where function of the numpy package. The condition of the where function is set to: the input data is less than 0.9 (in another embodiment, the measured data corresponds to the voltage rise fault of the wind turbine converter, and the condition of the where function is set to: the input data is greater than 1.1).

[0077] Step 3: As Figure 3 As shown, the steady-state start time tfaultQS after the reactive current fluctuation begins to enter the ±10% rated value range is determined 20ms after the fault, and the steady-state start time tclearQS after the fault is cleared. tbegin is the time one second before tfault, and tend is the time one second after tclearQS, thus realizing the interval marking of the data.

[0078] The data sequence is divided into three parts: Wpre before the fault, Wfault during the fault, and Wpost after the fault. Among them, tbegin is the start time of model verification during the fault crossing process; tfault is the fault start time; tfaultQS is the steady-state start time after the fault; tclear is the fault end time; tclearQS is the steady-state start time after the fault is cleared; and tend is the end time of the fault crossing process.

[0079] The interval from tbegin to tfault at the start of the fault is the pre-fault interval; the interval from tfaul at the start of the fault to tfaultQS at the start of the steady state after the fault is the transient interval during the fault period; the interval from tfaultQS at the start of the steady state after the fault to tclear at the end of the fault is the steady state interval during the fault period; the interval from tclear at the end of the fault to tclearQS at the start of the steady state after the fault is cleared is the transient interval after the fault; the interval from tclearQS at the start of the steady state after the fault is cleared to tend is the steady state interval after the fault, thus obtaining the measured marked data.

[0080] In this step, the steady-state start time tfaultQS and the steady-state start time tclearQS after fault clearance can also be determined according to other types of data (such as reactive power, active current, etc.), other fluctuation ranges (such as ±5% of the rated value or specifying the floating range by absolute value), and other preset range delays. Alternatively, tbegin and tent can be determined according to other estimated times. The above are just examples of data and are not limited in actual applications.

[0081] Step 4: Select or add the electromechanical transient simulation model of the converter of the corresponding model, obtain the power flow file and stability file corresponding to the electromechanical transient simulation model of this model of converter, and use the open file reading and writing function in Python to load the voltage value, maximum active power output, actual active power output, and arranged reactive power output into the power flow file to obtain an updated power flow file; set the initial value of the fault impedance X, and use the open file reading and writing function in Python to load the fault start time tfault in Step 2 and the initial value of the fault impedance X into the stability file to obtain an updated stability file; run the updated power flow file and stability file through the simulation software to obtain the simulation data of the electromechanical transient simulation model, and calculate the average effective voltage value Usim during the fault period (it can be: use the run function of the subprocess library to automatically run the power flow file first, and then run the stability file to extract the value Usim of the fundamental positive sequence voltage during the fault period in the simulation results). In this step or before this step, calculate the average effective voltage value Utest of the measured data during the fault period.

[0082] Select the impedance value of the short-circuit fault card in the stability file of the electromechanical transient simulation model through the bisection method until abs(Usim - Utest) < 0.001 (a preset threshold, and the specific value is not limited). The refined flowchart is as Figure 4 shown. The selected impedance value is the optimal impedance value. Load the optimal impedance value into the stability file to obtain an updated stability file. Run the updated power flow file and stability file through the simulation software to obtain the simulation data of the electromechanical transient simulation model, including the fundamental positive sequence component data, including voltage, active current, reactive current, active power, and reactive power data.

[0083] Step 5: Based on the fault start time tfault, make the simulation data correspond to the measured marked data one by one, and calculate the data deviation between the simulation data and the measured marked data for the pre-fault interval, transient interval during the fault, steady-state interval during the fault, post-fault transient interval, and post-fault steady-state interval respectively, including the mean error ME, mean absolute error MAE, maximum error MXE, and weighted mean absolute error XG (the data is shown in Table 1).

[0084] Table 1 Deviation table under the conditions of voltage drop to 35% Un, three-phase drop, and 10% Pn < P < 30% Pn

[0085]

[0086]

[0087] Based on the measured data and simulation data, use the matplotlib library to draw a verification graph, as Figure 5 shown. In the graph, BPA corresponds to the simulation data, and Test corresponds to the measured data.

[0088] The system uses a docx library to automatically paste images, create tables, interpolate, and format data. Based on preset templates, the docx library, verification diagrams, and data deviations, it generates a Word document and a verified electromechanical transient simulation model.

[0089] This invention selects the fault impedance of the electromechanical transient simulation model using a bisection method. The consistency between the voltage values ​​of the electromechanical transient simulation model data and the measured data during the fault period is used as the basis for selecting the fault impedance parameter, improving the accuracy of the electromechanical transient simulation model parameter settings and the verification precision of the model. Based on the obtained simulation data and measured data, the deviation of each interval is automatically calculated, and a verification plot is drawn using the matplotlib library. Using a customized template and the docx library, automatic plotting, tabulation, interpolation, and layout operations are performed, automatically generating a directly usable Word document and the verified electromechanical transient simulation model. This solution has the following advantages:

[0090] (1) It greatly improves the automation level of electromechanical transient simulation model verification, and solves the problem that the original verification method required manual completion of calculation, data cutting, interval division, model parameter setting, map and table pasting and document format adjustment. It achieves a significant improvement in verification efficiency, reducing the workload of several weeks or even months to less than 1 hour.

[0091] (2) It improves the accuracy of model verification and solves the problem that the interval division and impedance value adjustment in model verification are highly dependent on human subjective consciousness, with an accuracy of over 98%.

[0092] (3) This method and system are not limited to a single working condition and can complete the verification of any working condition during fault crossing in batches and quickly.

[0093] (4) This method and system are not limited to a single scenario and can be used for electromechanical transient simulation model verification of new energy wind turbine converter units, photovoltaic inverter units, reactive power compensation device SVG units, and energy storage converter units. The application range is wide.

[0094] (5) This invention lowers the technical threshold for model verification, enabling non-professionals to complete model verification work.

[0095] In one exemplary embodiment, similar to the previous embodiment, the difference is that the wind turbine converter can also be a photovoltaic inverter, a static var compensator (SVG), or an energy storage converter; and / or the difference is that the test condition voltage rise fault is tested.

[0096] In one exemplary embodiment, a computer device is provided, including data processing software and simulation software; the data processing software is used to execute an electromechanical transient simulation model verification method; the simulation software is used to acquire the electromechanical transient simulation model, measured data and fault start time corresponding to the target electromechanical equipment, and determine simulation data based on the electromechanical transient simulation model, measured data and fault start time corresponding to the target electromechanical equipment.

[0097] In an exemplary embodiment, the computer device in the previous embodiment may be a server or a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and databases.

[0098] The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The computer device's database stores data acquired by the electromechanical transient simulation model verification method, intermediate data during execution, and final result data after execution. The computer device's input / output interface is used for information exchange between the processor and external devices. The computer device's communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an electromechanical transient simulation model verification method.

[0099] Those skilled in the art will understand that Figure 6 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0100] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The device also includes a display module and a storage module. The display module is used to display a verification file and a visual interface for the implementation process of the above-described methods, including a data input interface and a setting interface for various thresholds, initial values, and / or specified data. The storage module is used to store the verification file and data for the implementation process of the above-described methods, including measured data, fault types, preset parameters, etc., input during the implementation process, as well as measured screening data, marking data, fault start time, fault end time, steady-state start time, steady-state start time after fault clearance, and first preset time, second preset time, third preset time, fourth preset time, first specified data, second specified data, first specified range, second specified range, etc., obtained during the implementation process. It also includes a verification file obtained after the implementation process of the electromechanical transient simulation model method, and the verified electromechanical transient simulation model.

[0101] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0102] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0103] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0104] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0105] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0106] In one exemplary embodiment, based on the same inventive concept, a Python-based electromechanical transient model verification system for new energy generating units is provided. This system includes a data preprocessing module, an electromechanical transient model library management module, a parameter verification module, and an automatic report generation module. The system block diagram is shown below. Figure 7 As shown, where:

[0107] Data preprocessing module: Capable of converting and reading different data formats, including mat, csv, xlsx, and txt; receives instantaneous voltage and current data from on-site fault voltage ride-through capability tests, and calculates the fundamental positive-sequence components to be verified using Fourier algorithms, including voltage, active power, reactive power, active current, and reactive current; automatically divides intervals, including the steady-state interval before the fault, the transient and steady-state intervals during the fault, and the transient and steady-state intervals after the fault; extracts initial key parameters, including fault start time, fault end time, voltage during the fault, and initial voltage, power, and current before the fault; similarly, it can also receive semi-physical test data, including voltage, active power, reactive power, active current, and reactive current extracted through calculation or direct extraction, and extract initial key parameters.

[0108] Electromechanical Transient Model Library Management Module: This module manages the electromechanical transient simulation model library. It allows users to search the library by model and manufacturer, add, modify, and delete models, and record information such as model model, manufacturer, date, and time when a model is added to the library. It also provides standardized and unified management of the electromechanical model library.

[0109] Parameter verification module: This module configures the environment for the power system analysis software PSD-BPA (PFNT.exe for power flow calculation and analysis) and (swnt.exe for stability calculation and analysis). It reads the power flow and stability files from PSD-BPA, loads the arranged voltage values, maximum active power output, and arranged reactive power output parameters in the power flow file based on the extracted initial parameters, and loads the model in the stability file. The module then automatically runs the power flow and stability files using the subprocess.run function to obtain electromechanical transient model simulation data.

[0110] Automatic report generation module: It can automatically extract SWX format data generated by PSD-BPA simulation, including voltage, active power, reactive power, active current and reactive current, and match them with the measured data; it can automatically calculate the deviation according to the divided intervals, including average deviation, average absolute deviation, maximum deviation and weighted average absolute deviation; it can automatically generate verification charts and deviation tables, and the chart format can be adjusted according to templates or custom formats, and automatically generate a ready-to-use Word report.

[0111] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0112] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for verifying an electromechanical transient simulation model, characterized in that, The verification method for the electromechanical transient simulation model includes: Obtain measured data of the target electromechanical equipment; The measured data is filtered using an inflection point algorithm to obtain filtered measured data, and the fault start time and fault end time of the filtered measured data are determined. The measured filtered data are marked using the fault start time and the fault end time to obtain measured marked data; Acquire simulation data from the electromechanical transient simulation model; the simulation data is based on simulation software, the electromechanical transient simulation model corresponding to the target electromechanical equipment, the measured data, and the data determined by the fault start time; The simulation data is compared with the measured marked data to obtain a verification file.

2. The electromechanical transient simulation model verification method according to claim 1, characterized in that, The step of using the inflection point algorithm to filter the measured data to obtain filtered measured data specifically includes: The measured data are preprocessed using the Fourier algorithm to obtain fundamental positive sequence component data; the measured data are instantaneous data of phase voltage and phase current; the fundamental positive sequence component data includes voltage, active power, reactive power, active current and reactive current; The voltage data in the fundamental positive sequence component data is filtered using the inflection point detection function of the ruptures package to obtain the measured filtered data; wherein, the loss function model of the inflection point detection function is set to l2 and the number of inflection points n_bkps is set to 2. The measured screening data is the data from 2 seconds before the fault to 2 seconds after the fault.

3. The electromechanical transient simulation model verification method according to claim 1, characterized in that, Determining the fault start time and fault end time of the measured and screened data specifically includes: The fault start time and fault end time of the measured filtered data are determined using the where function of the numpy package. The condition of the where function is set as follows: the input data is less than a first preset parameter, or the input data is greater than a second preset parameter.

4. The electromechanical transient simulation model verification method according to claim 1, characterized in that, The measured marked data is divided into five intervals: the pre-fault interval, the transient interval during the fault, the steady-state interval during the fault, the post-fault transient interval, and the post-fault steady-state interval. The measured filtered data is marked using the fault start time and the fault end time to obtain measured marked data, including: The following methods are used to obtain the steady-state start time after a fault and the steady-state start time after fault clearance. The steady-state start time after a fault is the time corresponding to a first time after a first preset time, where the first time is the time when the fluctuation of a first specified data enters a first specified range. The steady-state start time after fault clearance is the time corresponding to a second time after a second preset time, where the second time is the time when the fluctuation of a second specified data enters a second specified range. The steady-state start time after a fault is between the fault start time and the fault end time, and the steady-state start time after fault clearance is after the fault end time. The data interval from the third preset time before the fault start time to the fault start time is marked as the pre-fault interval; The data interval from the fault start time to the steady-state start time after the fault is marked as the transient interval during the fault period; The data interval from the start of the steady state after the fault to the end of the fault is marked as the steady-state interval during the fault period; The data interval from the time the fault ended to the time the steady state began after the fault was cleared is the post-fault transient interval. The interval data from the start time of the steady state after the fault is cleared to the fourth preset time after the start time of the steady state after the fault is cleared is the steady state interval after the fault.

5. The electromechanical transient simulation model verification method according to claim 1, characterized in that, The process for determining the simulation data is as follows: Determine the power flow file and stability file of the electromechanical transient simulation model corresponding to the target electromechanical equipment; Using the open file read / write function in Python, the voltage value, maximum active power output, actual active power output, and scheduled reactive power output are loaded into the power flow file based on the measured data, resulting in an updated power flow file. Using the open file read / write function in Python, the fault start time and fault impedance X value are loaded into the stability file to obtain the updated stability file; The simulation data of the electromechanical transient simulation model is obtained by running the updated power flow file and stability file using simulation software.

6. The electromechanical transient simulation model verification method according to claim 5, characterized in that, The simulation data of the electromechanical transient simulation model obtained by running the updated power flow file and stability file through simulation software specifically includes: S1. Set the initial value of the fault impedance X of the electromechanical transient simulation model and determine the fault type based on the measured data; wherein, Xmax≥X≥Xmin, the initial value of Xmax is the maximum fault impedance supported by the electromechanical transient simulation model, and the initial value of Xmin is the minimum fault impedance supported by the electromechanical transient simulation model. S2. The average effective value of the measured screening data during the fault period is determined as the first voltage; S3. Run the updated power flow file and stability file corresponding to the current stage through simulation software to obtain the simulation data of the current stage corresponding to the electromechanical transient simulation model, and determine the average effective value of the voltage during the fault period of the current stage simulation data as the second voltage. S4. Determine whether the difference between the first voltage and the second voltage is less than a preset threshold, and obtain the first determination result; S5. When the first judgment result indicates that the difference between the first voltage and the second voltage is less than a preset threshold, execute S9; S6. When the first judgment result indicates that the difference between the first voltage and the second voltage is not less than the preset threshold, and the fault type is voltage drop, execute S7; when the first judgment result indicates that the difference between the first voltage and the second voltage is not less than the preset threshold, and the fault type is voltage rise, execute S8. S7. When the first voltage is greater than the second voltage, update Xmax to the fault impedance X, update the fault impedance X corresponding to the current stage according to the first formula to obtain the updated fault impedance X, update the updated power flow file and stability file corresponding to the current stage based on the updated fault impedance X, and return to S3; when the first voltage is not greater than the second voltage, update Xmin to the fault impedance X, update the fault impedance X corresponding to the current stage according to the first formula to obtain the updated fault impedance X, update the updated power flow file and stability file corresponding to the current stage based on the updated fault impedance X, and return to S3. The first formula is: Where X is the updated fault impedance; when Xmax1 is the updated Xmax, Xmin1 is Xmin; when Xmax1 is Xmax, Xmin1 is the updated Xmin. S8. When the first voltage is greater than the second voltage, update Xmin to the fault impedance X, update the fault impedance X corresponding to the current stage according to the first formula to obtain the updated fault impedance X, update the updated power flow file and stability file corresponding to the current stage based on the updated fault impedance X, and return to S3; when the first voltage is greater than or equal to the second voltage, update Xmax to the fault impedance X, update the fault impedance X corresponding to the current stage according to the first formula to obtain the updated fault impedance X, update the updated power flow file and stability file corresponding to the current stage based on the updated fault impedance X, and return to S3. S9. Determine the current stage simulation data as the final simulation data.

7. The electromechanical transient simulation model verification method according to claim 1, characterized in that, The verification file includes a Word document and the verified electromechanical transient simulation model; the measured marked data is divided into five intervals, namely the pre-fault interval, the transient interval during the fault, the steady-state interval during the fault, the post-fault transient interval, and the post-fault steady-state interval. The simulation data is verified against the measured marked data to obtain the verification file, which specifically includes: Based on the fault start time, the simulation data and the measured marked data are matched one-to-one. The data deviation between the simulation data and the measured marked data is calculated for the pre-fault interval, the transient interval during the fault, the steady-state interval during the fault, the post-fault transient interval, and the post-fault steady-state interval. The data deviation includes the average deviation, the average absolute deviation, the maximum deviation, and the weighted average absolute deviation. A verification chart was drawn based on the matplotlib library, the simulation data, and the measured labeled data. Based on the preset template, the docx library, the verification diagram, and the data deviation, a Word document and the verified electromechanical transient simulation model are generated.

8. A computer device, characterized in that, include: Data processing software and simulation software; The data processing software is used to execute the electromechanical transient simulation model verification method according to any one of claims 1-7; The simulation software is used to acquire the electromechanical transient simulation model, measured data, and fault start time corresponding to the target electromechanical equipment, and to determine the simulation data based on the electromechanical transient simulation model, measured data, and fault start time corresponding to the target electromechanical equipment.

9. A computer device according to claim 8, characterized in that... It also includes a display module and a storage module; the display module is used to display the verification file; the storage module is used to store the verification file.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the electromechanical transient simulation model verification method according to any one of claims 1-7.

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