Method and device for determining index evaluation model of converter, and storage medium

Abstract

The application provides a method and device for determining an index evaluation model of a converter, and a storage medium, the method comprising: configuring an executable script file; executing the executable script file to generate at least one set of target control parameter sets, the target control parameter sets including control parameters of multiple converters; performing simulation tests on each target control parameter set under a target scenario to obtain corresponding performance index data, the performance index data being data representing the running state of the converter when running under the target scenario using the control parameters in the target control parameter set; and training a to-be-trained neural network using the target control parameter set and the corresponding performance index data to obtain a digital twin model, the digital twin model being used to evaluate the performance index of the converter. The method improves the construction method of the relationship between the control parameters of the converter and the performance index, and quantitatively describes the influence of the control parameters of the converter on the performance index with short time consumption and high accuracy.

Description

Technical Field

[0001] This application relates to the field of converter control systems, and more specifically, to a method for determining a converter performance evaluation model, a device for determining a converter performance evaluation model, a computer-readable storage medium, and an electronic device. Background Technology

[0002] With the increasing scale of renewable energy deployment, energy storage converters are being used more and more widely in AC / DC hybrid power systems. While the increased level of power electronics improves system regulation and response speed, it also weakens and reduces system inertia and damping, posing a significant challenge to stable system operation. Therefore, the importance of converter control system design is becoming increasingly prominent. For traditional energy storage converters, depending on the application scenario, their control systems need to consider objectives such as power reference tracking and DC bus voltage control.

[0003] The control parameters and performance indicators of a converter, especially dynamic performance indicators such as overshoot and rise time, typically do not have a direct linear relationship, making them difficult to quantitatively characterize through theoretical studies. Therefore, existing solutions often involve quantitatively evaluating multiple sets of different control parameters through computer software simulation and converter hardware experiments. Because this requires exhaustive evaluation of different control parameters, traditional solutions necessitate extensive simulation or hardware testing, which is time-consuming and inefficient. Summary of the Invention

[0004] The main objective of this application is to provide a method for determining the performance evaluation model of a converter, a device for determining the performance evaluation model of a converter, a computer-readable storage medium, and an electronic device, so as to at least solve the problem that existing methods for determining converter control parameters require a large number of simulations or hardware experiments, which are time-consuming and inefficient.

[0005] To achieve the above objectives, according to one aspect of this application, a method for determining an evaluation model for a converter is provided, comprising: configuring an executable script file; executing the executable script file to generate at least one set of target control parameters, the set of target control parameters including multiple control parameters of the converter, the control parameters being parameters for controlling the operation of the converter; performing simulation tests on each set of target control parameters under a target scenario to obtain corresponding performance index data, the performance index data being data characterizing the operating state of the converter under the target scenario using the control parameters in the set of target control parameters; training a neural network to be trained using the set of target control parameters and the corresponding performance index data to obtain a digital twin model, the digital twin model being used to evaluate the performance index of the converter.

[0006] Optionally, executing the executable script file to generate at least one set of target control parameters includes: obtaining a preset parameter scale, the preset parameter scale including: a preset number of control parameter sets and a preset value range for each control parameter; executing the executable script file based on the preset parameter scale to generate an initial control parameter set, wherein the values ​​of the control parameters in the initial control parameter set are all within the corresponding preset value range; determining whether the number of generated initial control parameter sets has reached the preset number; if the number of generated initial control parameter sets has reached the preset number, determining all generated initial control parameter sets as the target control parameter set; if the number of generated initial control parameter sets has not reached the preset number, based on the preset parameter scale and the principle of independent and identically distributed, continuing to execute the executable script file to generate the initial control parameter set again.

[0007] Optionally, simulation tests are performed on each of the target control parameter sets under a target scenario to obtain corresponding performance index data, including: sequentially inputting each of the target control parameter sets into a converter control simulation system for simulation testing; setting the test scenario as the target scenario, and sequentially performing simulation tests on each of the target control parameter sets under the target scenario; obtaining key simulation waveform data obtained from the simulation tests on each of the target control parameter sets, wherein the target control parameter sets and the key simulation waveform data correspond one-to-one; and processing the key simulation waveform data to obtain performance index data corresponding to each of the target control parameter sets.

[0008] Optionally, the key simulation waveform data includes the active power step response waveform file and the reactive power step response waveform file of the converter. The key simulation waveform data is processed to obtain the performance index data corresponding to each set of target control parameters. This includes processing the active power step response waveform file and the reactive power step response waveform file of the converter to obtain the rise time of the active power, the cumulative integral error of the active power, the percentage overshoot of the active power, and the rise time of the reactive power, the cumulative integral error of the reactive power, and the percentage overshoot of the reactive power, so as to obtain the performance index data.

[0009] Optionally, training the neural network to be trained using the target control parameter set and the corresponding performance index data to obtain a digital twin model includes: determining the dimension of the data to be fitted based on the number of control parameters in one of the target control parameter sets, and determining the amount of training data based on the data in all the target control parameter sets; determining the structure of the neural network to be trained based on the dimension of the data to be fitted and the amount of training data, wherein the structure of the neural network to be trained includes at least the number of neural network layers and the number of neurons in each neural network layer; using the control parameters in all the target control parameter sets as inputs and the performance index data corresponding to each target control parameter set as outputs to train and fit the neural network to be trained to obtain the digital twin model.

[0010] Optionally, the control parameters of the converter include: PI parameters of the voltage loop based on dual-loop control and PI parameters of the current loop based on dual-loop control, where P is the proportional coefficient and I is the integral coefficient.

[0011] Optionally, after training the neural network to be trained using the target control parameter set and the corresponding performance index data to obtain a digital twin model, the method further includes: obtaining the actual control parameters of the converter at the current moment, wherein the actual control parameters are the control parameters used by the converter in actual operation at the current moment; inputting the actual control parameters into the digital twin model to obtain the actual performance index of the converter, wherein the actual performance index is performance data characterizing the actual operating state of the converter at the current moment; and determining the current operating state of the converter based on the actual performance index of the converter.

[0012] According to another aspect of this application, an apparatus for determining an evaluation model for a converter is provided, comprising: a first processing unit for configuring an executable script file for generating control parameters of the converter, the control parameters being parameters for controlling the operation of the converter; a second processing unit for executing the executable script file to generate at least one set of target control parameters, the target control parameter set including multiple control parameters; a third processing unit for performing simulation tests on each of the target control parameter sets under a target scenario to obtain corresponding performance index data, the performance index data being data characterizing the operating state of the converter under the target scenario using the control parameters in the target control parameter set; and a fourth processing unit for training a neural network to be trained using the target control parameter set and the corresponding performance index data to obtain a digital twin model, the digital twin model being used to evaluate the performance index of the converter.

[0013] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to execute a method for determining any of the aforementioned converter index evaluation models.

[0014] According to another aspect of this application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a method for determining an index evaluation model for executing any of the above-described converters.

[0015] Applying the technical solution of this application, the method for determining the performance evaluation model of the converter described above first configures an executable script file; then executes the executable script file to generate at least one set of target control parameters, which includes multiple control parameters of the converter, and the control parameters are parameters for controlling the operation of the converter; then, simulation tests are performed on each target control parameter set under the target scenario to obtain the corresponding performance index data, which represents the operating state of the converter under the target scenario using the control parameters in the target control parameter set; finally, the target control parameter set and the corresponding performance index data are used to train the neural network to obtain a digital twin model, which is used to evaluate the performance index of the converter. This method improves the construction of the relationship between converter control parameters and performance indicators, quantitatively describing the impact of converter control parameters on performance indicators with short processing time and high accuracy. This provides guidance and basis for the design of converter control systems. Compared with the traditional exhaustive control parameter performance evaluation method based on a large number of software simulations / experimental tests, it can significantly reduce the required time cost while ensuring accuracy. It can be used for the design of converter control systems in new power systems with a high degree of power electronics, solving the problem that existing methods for determining converter control parameters require a large number of simulations or hardware experiments, which are time-consuming and inefficient. Attached Figure Description

[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 A hardware block diagram of a mobile terminal for determining a method for evaluating the performance indicators of a converter, according to an embodiment of this application, is shown.

[0018] Figure 2A flowchart illustrating a method for determining an index evaluation model for a converter according to an embodiment of this application is shown.

[0019] Figure 3 A topology diagram of an MMC energy storage converter provided according to an embodiment of this application is shown;

[0020] Figure 4 A block diagram of an energy storage MMC converter control provided according to an embodiment of this application is shown;

[0021] Figure 5 A neural network structure diagram according to an embodiment of this application is shown;

[0022] Figure 6 A schematic diagram illustrating the accuracy of the cumulative integral error model provided according to an embodiment of this application is shown;

[0023] Figure 7 A schematic diagram illustrating the accuracy of the overshoot model provided according to an embodiment of this application is shown;

[0024] Figure 8 A schematic diagram illustrating the accuracy of the rise time model provided according to an embodiment of this application is shown;

[0025] Figure 9 A schematic diagram illustrating the indices for cumulative integral error, overshoot, and rise time fitting provided according to embodiments of this application is shown.

[0026] Figure 10 A schematic diagram of a system for constructing a digital twin model between converter control parameters and performance indicators according to an embodiment of this application is shown.

[0027] Figure 11 A flowchart illustrating another method for determining an index evaluation model for a converter according to an embodiment of this application is shown.

[0028] Figure 12 A structural block diagram of a device for determining an index evaluation model for a converter, according to an embodiment of this application, is shown.

[0029] The above figures include the following reference numerals:

[0030] 11. Executable script file; 12. Control parameter set generation module; 13. Converter control simulation system; 14. Performance index synthesis module; 15. Neural network trainer; 102. Processor; 104. Memory; 106. Transmission equipment; 108. Input / output devices. Detailed Implementation

[0031] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0032] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] For ease of description, the following explains some of the nouns or terms used in the embodiments of this application:

[0035] A converter is a power electronic device used to convert one form of electrical energy into another. It plays a key role in modern power electronics and power transmission systems, improving energy efficiency, reducing energy consumption, and enabling flexible control and management of electrical energy.

[0036] Digital twins are an advanced digital technology that creates a virtual model of a physical object, system, or process; they are digital copies of physical entities.

[0037] As described in the background section, the Modular Multilevel Converter (MMC) energy storage converter is an advanced power electronic device capable of efficiently converting between alternating current (AC) and direct current (DC). It plays a crucial role in energy storage systems, combining with batteries or supercapacitors to store and release energy, providing additional power during peak demand periods and storing excess energy during off-peak periods, thereby balancing grid load. Furthermore, MMC energy storage converters can support the integration of renewable energy sources, improve grid stability and power quality, and are widely used in modern power systems such as smart grids and microgrids.

[0038] For traditional energy storage converters, the control system needs to consider objectives such as power reference tracking and DC bus voltage control, depending on the application scenario. Taking the most common power reference tracking as an example, the converter control system typically uses a dual-loop feedback control method to control the active and reactive power on the AC side, including outer and inner loop control: the outer loop control includes two controllers, usually proportional integral controllers (PI), whose inputs are the tracking errors of active and reactive power, and whose output is the reference of the two components of the AC phase current of the converter in the dq (direct-quadrature) coordinate system; the inner loop control is also usually based on a PI controller, tracking the current reference given by the outer loop control, and its output is used as a voltage reference signal input to the modulation module of the converter.

[0039] Furthermore, to improve converter operation and enhance its performance, additional control modules or components are typically introduced into the converter control system. For example, when the active / reactive power reference changes, the converter is prone to overshoot and oscillation during step transients. To address this issue, an adaptive power reference module can be introduced to adaptively and dynamically adjust the outer loop active / reactive power reference of the aforementioned dual-loop feedback controller. These newly added control components and modules introduce additional control parameters while improving control performance.

[0040] To address the problem that existing methods for determining converter control parameters require extensive simulations or hardware experiments, which are time-consuming and inefficient, embodiments of this application provide a method for determining a converter performance evaluation model, a device for determining a converter performance evaluation model, a computer-readable storage medium, and an electronic device.

[0041] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0042] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a method of determining the performance evaluation model of a converter according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0043] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the method for determining the indicator evaluation model of the converter in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-described networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the above-described networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0044] This embodiment provides a method for determining the performance evaluation model of a converter running on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0045] Figure 2 This is a flowchart illustrating a method for determining the performance evaluation model of a converter according to an embodiment of this application. Figure 2 As shown, the method includes the following steps:

[0046] Step S201: Configure the executable script file;

[0047] Specifically, an executable file refers to a script file in real-time simulation software (RTDS) for power systems. It is implemented using a C-like language and is a script-based file.

[0048] The script file generates control parameters, a set of parameters that are within the control parameter range and are obtained by random uniform distribution.

[0049] Step S202: Execute the above-mentioned executable script file to generate at least one set of target control parameters, the target control parameter set including multiple control parameters of the above-mentioned converter, the control parameters being parameters for controlling the operation of the converter;

[0050] Specifically, generating multiple sets of target control parameters can help optimize converter operation, improve its efficiency and performance, better understand the converter's operating mechanism, and enhance the ability to control and adjust its parameters.

[0051] in, Figure 3 A topology diagram of an MMC energy storage converter is shown, as follows: Figure 3 As shown, a modular multilevel converter topology is adopted, with 20 sub-modules on each bridge arm. The DC power on the sub-modules is converted into AC power and transmitted to the power grid through switching signals.

[0052] Figure 4 For based on Figure 3 The control block diagram of the energy storage MMC converter, such as Figure 4 As shown, active and reactive power are calculated by collecting voltage and current signals from the grid side and used as input signals for the controller. Modulation wave signals are generated through calculations in the power outer loop and current inner loop, and finally converted into switching signals for the modular multilevel converter after modulation.

[0053] The control parameters of the aforementioned converter include: PI parameters for the voltage loop based on dual-loop control and PI parameters for the current loop based on dual-loop control, where P is the proportional coefficient and I is the integral coefficient. The control parameters of the aforementioned converter are as follows: Figure 4 In this context, kp1, ki1, kp2, and ki2 represent a combination of values ​​for each of the four control parameters. The value range refers to the range of values ​​for each control parameter.

[0054] Specifically, by properly adjusting the PI parameter, the system can respond faster, achieve a quicker control response, and effectively reduce system overshoot and oscillation, thereby improving system stability.

[0055] Executing the aforementioned executable script file to generate at least one set of target control parameters includes the following steps:

[0056] Step S301: Obtain the preset parameter scale, which includes: the preset number of control parameter sets and the preset value range of each of the above control parameters;

[0057] Step S302: Execute the executable script file based on the preset parameter scale to generate an initial control parameter set. The values ​​of the control parameters in the initial control parameter set are all within the corresponding preset value range.

[0058] Step S303: Determine whether the number of the generated initial control parameter sets has reached the preset number.

[0059] Step S304: If the number of generated initial control parameter sets reaches the preset number, all generated initial control parameter sets are determined as the target control parameter set.

[0060] Step S305: If the number of the generated initial control parameter sets has not reached the preset number, based on the preset parameter size and the principle of independent and identical distribution, the executable script file is executed again to generate the initial control parameter sets.

[0061] Specifically, digital twin technology enables the quantitative description and characterization of the relationship between converter control parameters and performance indicators, automatically generating control parameter sets, reducing the need for manual intervention, improving efficiency, and the preset scale is often set based on human experience.

[0062] The executable file is executed in a loop and stops running after the required number of groups is reached. The performance index set is generated in a loop. If not enough groups and performance indices are generated, only the control parameter set needs to be generated.

[0063] Step S203: Perform simulation tests on each of the above target control parameter sets under the target scenario to obtain the corresponding performance index data. The above performance index data is data that characterizes the operating status of the converter under the above target scenario when the above control parameters in the above target control parameter set are used.

[0064] Specifically, simulation testing can quickly acquire a large amount of data, which can then be analyzed and compared to quickly find the optimal control parameters and improve the performance and efficiency of the converter.

[0065] The process of performing simulation tests on each of the aforementioned target control parameter sets under the target scenario to obtain the corresponding performance index data includes the following steps:

[0066] Step S401: Input the above target control parameter sets into the converter control simulation system in sequence for simulation testing;

[0067] Step S402: Set the test scenario as the above-mentioned target scenario, and perform simulation tests on each of the above-mentioned target control parameter sets under the above-mentioned target scenario in sequence.

[0068] Step S403: Obtain key simulation waveform data from simulation tests of each of the above target control parameter sets, where each of the above target control parameter sets corresponds one-to-one with the above key simulation waveform data.

[0069] Step S404: Process the above key simulation waveform data to obtain the performance index data corresponding to each of the above target control parameter sets.

[0070] The aforementioned key simulation waveform data includes the active power step response waveform file and the reactive power step response waveform file of the converter. Processing this key simulation waveform data yields the performance index data corresponding to each of the aforementioned target control parameter sets, including:

[0071] The active power step response waveform file and the reactive power step response waveform file of the above-mentioned converter are processed to obtain the rise time of the active power, the cumulative integral error of the active power, the percentage overshoot of the active power, the rise time of the reactive power, the cumulative integral error of the reactive power, and the percentage overshoot of the reactive power, so as to obtain the above-mentioned performance index data.

[0072] Specifically, by processing waveform files, the performance index data of the converter can be obtained directly without manual calculation and analysis, reducing the possibility of human error and improving the accuracy and efficiency of data processing.

[0073] Specifically, by analyzing performance index data, the optimal set of control parameters can be identified, thereby optimizing system performance. Furthermore, simulation tests of different parameter combinations can be performed quickly, improving testing efficiency.

[0074] Step S204: The target control parameter set and the corresponding performance index data are used to train the neural network to obtain a digital twin model. The digital twin model is used to evaluate the performance index of the converter.

[0075] Specifically, digital twin models can be trained on real-world data and are able to predict converter performance metrics more accurately. Continuous model optimization can further improve the accuracy of evaluation results.

[0076] The process of training the neural network to obtain a digital twin model using the aforementioned set of target control parameters and the corresponding performance index data includes the following steps:

[0077] Step S501: Determine the dimension of the data to be fitted based on the number of control parameters in one of the target control parameter sets, and determine the amount of training data based on the data in all the target control parameter sets.

[0078] Step S502: Based on the dimensions of the data to be fitted and the amount of training data, determine the structure of the neural network to be trained. The structure of the neural network to be trained includes at least the number of neural network layers and the number of neurons in each neural network layer.

[0079] Among them, according to Figure 4 and Figure 5 As shown, Figure 5 A neural network structure diagram is shown. This neural network includes an input layer, hidden layers, and an output layer. Since the input data consists of four data points and the output is a set of indicator data, the input layer has a dimension of four, and the output layer has a dimension of one. The data dimension is actually the dimension of the control parameters, i.e., the four input parameters, which are... Figure 4 The dataset consists of kp1, ki1, kp2, ki2, and an output parameter. The data size is determined by the hold-out method, with 20% of the data set used as the validation set and 80% used as the training set.

[0080] Step S503: Using all the control parameters in the above-mentioned target control parameter sets as inputs and the performance index data corresponding to each of the above-mentioned target control parameter sets as outputs, train and fit the above-mentioned neural network to obtain the above-mentioned digital twin model.

[0081] Specifically, neural network models are used to fit complex multi-parameter systems, thereby enabling the prediction and optimization of system behavior. The trained digital twin model can quickly and accurately simulate and analyze the system, helping to optimize system control parameters.

[0082] Figure 6 A schematic diagram illustrating the accuracy of a cumulative integral error model is shown. Figure 7 A schematic diagram illustrating the accuracy of an overshoot model is shown. Figure 8 A schematic diagram illustrating the accuracy of a rise time model is shown.

[0083] In some instances, such as Figure 6 As shown, Figure 6 (a) and Figure 6 (b) are the cumulative integral error responses of active power and reactive power, respectively. Figure 6 (a) The horizontal axis represents the actual value of the integrated active power cumulative error of the input, and the vertical axis represents the predicted value of the integrated active power cumulative error of the output. Figure 6 (b) The horizontal axis represents the actual value of the input reactive power cumulative error integral, and the vertical axis represents the predicted value of the output reactive power cumulative error integral. Points falling on the straight line indicate that the prediction result is accurate.

[0084] like Figure 7 As shown, Figure 7 (a) and Figure 7 (b) are the accuracy responses of the overshoot model for active power and reactive power, respectively. Figure 7 (a) The horizontal axis represents the actual value of the input active power percentage overshoot, and the vertical axis represents the predicted value of the output active power percentage overshoot. Figure 7 (b) The horizontal axis represents the actual value of the input reactive power percentage overshoot, and the vertical axis represents the predicted value of the output reactive power percentage overshoot. Points falling on the straight line indicate that the prediction result is accurate.

[0085] like Figure 8 As shown, Figure 8 (a) and Figure 8 (b) are the rise time model accuracy responses for active power and reactive power, respectively. Figure 8 (a) The horizontal axis represents the actual value of the rise time of the input active power, and the vertical axis represents the predicted value of the rise time of the output active power. Figure 8 (b) The horizontal axis represents the actual value of the rise time of the input reactive power, and the vertical axis represents the predicted value of the rise time of the output reactive power. Points falling on the straight line indicate that the prediction result is accurate.

[0086] in, Figure 6 , Figure 7 , Figure 8 Used to illustrate the effect of the fit.

[0087] Based on the collected waveform data, the rise time, cumulative integral error, and percentage overshoot are obtained through time-domain indicators. Figure 9 A schematic diagram illustrating the indices for cumulative integral error, overshoot, and rise time fitting provided according to embodiments of this application is shown. Figure 9 It is also divided into active power index introduction and reactive power index introduction. Among them, t1-t0 is the rise time, the cumulative integral error of the shaded area, and a / b is the percentage overshoot.

[0088] After training the neural network to be trained using the aforementioned set of target control parameters and the corresponding performance index data to obtain the digital twin model, the above method further includes the following steps:

[0089] Step S601: Obtain the actual control parameters of the converter at the current moment. The actual control parameters are the control parameters used by the converter during actual operation at the current moment.

[0090] Step S602: Input the above-mentioned actual control parameters into the above-mentioned digital twin model to obtain the actual performance index of the above-mentioned converter. The above-mentioned actual performance index is the performance data that characterizes the actual operating state of the above-mentioned converter at the current moment.

[0091] Step S603: Determine the current operating status of the converter based on its actual performance indicators.

[0092] Specifically, digital twin models simulate and analyze the actual control parameters of the converter, which can more accurately characterize the actual operating state of the converter, improve the accuracy of judgment, and significantly reduce the required time cost while ensuring accuracy.

[0093] The method for determining the performance evaluation model of the converter described in this application first configures an executable script file; then, the executable script file is executed to generate at least one set of target control parameters, which includes multiple control parameters for the converter, and these control parameters are parameters used to control the operation of the converter; then, simulation tests are performed on each target control parameter set under a target scenario to obtain corresponding performance index data, which characterizes the operating state of the converter under the target scenario using the control parameters in the target control parameter set; finally, the target control parameter set and the corresponding performance index data are used to train the neural network to obtain a digital twin model, which is used to evaluate the performance index of the converter. This method improves the construction of the relationship between converter control parameters and performance indicators, quantitatively describing the impact of converter control parameters on performance indicators with short processing time and high accuracy. This provides guidance and basis for the design of converter control systems. Compared with the traditional exhaustive control parameter performance evaluation method based on a large number of software simulations / experimental tests, it can significantly reduce the required time cost while ensuring accuracy. It can be used for the design of converter control systems in new power systems with a high degree of power electronics, solving the problem that existing methods for determining converter control parameters require a large number of simulations or hardware experiments, which are time-consuming and inefficient.

[0094] Figure 10 This is a system for constructing a digital twin model between converter control parameters and performance indicators, such as... Figure 10As shown, the system includes an executable script file 11, a control parameter set generation module 12, a converter control simulation system 13, a performance index synthesis module 14, and a neural network trainer 15. By configuring the executable script file 11, the control parameter set generation module 12, the converter control simulation system 13, and the performance index synthesis module 14 are invoked to obtain performance index data of the converter controller under specific control parameters. The control parameter set generation module 12 generates specific control parameters to be tested within a specified parameter value range, and then inputs them to the converter control simulation system 13 for simulation testing under specific scenarios. Key simulation waveform data is output to the performance index synthesis module 14, which extracts and calculates performance indicators from the key simulation waveform data to evaluate the control performance under specific converter control parameters and stores the results. Then, the neural network trainer 15 uses the control parameter set generated by the control parameter set generation module 12 as input and the corresponding performance index set synthesized and stored by the performance index synthesis module 14 as output to perform neural network fitting.

[0095] To enable those skilled in the art to better understand the technical solution of this application, the implementation process of the method for determining the index evaluation model of the converter in this application will be described in detail below with reference to specific embodiments.

[0096] This embodiment relates to a specific method for determining the performance evaluation model of a converter, such as... Figure 11 As shown, it includes the following steps:

[0097] Step S1: Determine the number of control parameter groups to be generated and their value range;

[0098] Step S2: Configure the executable script file;

[0099] Step S3: Determine whether the number of generated control parameter groups has reached the preset scale; if it has, proceed to step S8; otherwise, execute step S4.

[0100] Step S4: Within the range of converter control parameter values ​​preset in step S1, generate a new set of control parameters according to the principle of independent and identical distribution;

[0101] Step S5: Apply the newly generated set of control parameters from step S4 to the converter control simulation system and perform simulation tests under a specific performance evaluation scenario;

[0102] Step S6: Obtain the key simulation waveform data corresponding to the simulation test in step S5;

[0103] Step S7: Process the waveform data obtained in step S6, calculate and store the performance indicators, and then proceed to step S3;

[0104] Step S8: Determine the structure of the neural network to be trained based on the dimension and amount of data to be fitted, including the number of neural network layers and the number of neurons in each layer.

[0105] Step S9: Using the set of all control parameters generated in step S4 and the set of corresponding performance indices synthesized in step S7 as input and output respectively, train and fit the neural network with the structure determined in step S8.

[0106] Step S10: Generate a digital twin model describing the relationship between converter control parameters and performance indicators;

[0107] Step S11: End.

[0108] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0109] This application also provides an apparatus for determining a converter performance evaluation model. It should be noted that this apparatus can be used to execute the method for determining a converter performance evaluation model provided in this application. This apparatus is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0110] The following describes the apparatus for determining the performance evaluation model of a converter provided in the embodiments of this application.

[0111] Figure 12 This is a schematic diagram of a device for determining the performance evaluation model of a converter according to an embodiment of this application. Figure 12As shown, the device includes: a first processing unit 10, a second processing unit 20, a third processing unit 30, and a fourth processing unit 40. The first processing unit 10 is used to configure an executable script file, which is used to generate control parameters for the converter. The control parameters are parameters for controlling the operation of the converter. The second processing unit 20 is used to execute the executable script file to generate at least one set of target control parameters, which includes multiple control parameters. The third processing unit 30 is used to perform simulation tests on each of the target control parameter sets under target scenarios to obtain corresponding performance index data. The performance index data is data characterizing the operating state of the converter under the target scenario using the control parameters in the target control parameter set. The fourth processing unit 40 is used to train a neural network to be trained using the target control parameter set and the corresponding performance index data to obtain a digital twin model. The digital twin model is used to evaluate the performance index of the converter.

[0112] The apparatus for determining the performance evaluation model of the converter described in this application includes: a first processing unit, a second processing unit, a third processing unit, and a fourth processing unit. The first processing unit is used to configure an executable script file, which generates control parameters for the converter, the control parameters being parameters for controlling the operation of the converter. The second processing unit is used to execute the executable script file to generate at least one set of target control parameters, the target control parameter set including multiple control parameters. The third processing unit is used to perform simulation tests on each target control parameter set under a target scenario to obtain corresponding performance index data, the performance index data being data characterizing the operating state of the converter under the target scenario using the control parameters in the target control parameter set. The fourth processing unit is used to train a neural network to be trained using the target control parameter set and the corresponding performance index data to obtain a digital twin model, the digital twin model being used to evaluate the performance index of the converter. This device improves the method for constructing the relationship between converter control parameters and performance indicators, quantitatively describing the impact of converter control parameters on performance indicators with short processing time and high accuracy. This provides guidance and basis for the design of converter control systems. Compared with the traditional exhaustive control parameter performance evaluation method based on a large number of software simulations / experimental tests, it can significantly reduce the required time cost while ensuring accuracy. It can be used for the design of converter control systems in new power systems with a high degree of power electronics, solving the problem that existing methods for determining converter control parameters require a large number of simulations or hardware experimental tests, which are time-consuming and inefficient.

[0113] In some examples, the second processing unit includes a first acquisition module, a first processing module, a first determination module, and a second determination module. The first acquisition module is used to acquire a preset parameter scale, which includes a preset number of control parameter sets and a preset value range for each control parameter. The first processing module is used to execute the executable script file based on the preset parameter scale to generate an initial control parameter set, wherein the values ​​of the control parameters in the initial control parameter set are all within the corresponding preset value ranges. The first determination module is used to determine whether the number of generated initial control parameter sets has reached the preset number. If the number of generated initial control parameter sets has reached the preset number, all generated initial control parameter sets are determined as the target control parameter set. If the number of generated initial control parameter sets has not reached the preset number, the second determination module is used to continue executing the executable script file to generate the initial control parameter set again, based on the preset parameter scale and the principle of independent and identically distributed control parameters. By using digital twin technology, the relationship between converter control parameters and performance indicators can be quantitatively described and characterized, and control parameter sets can be automatically generated, reducing the need for manual intervention and improving efficiency.

[0114] In some examples, the third processing unit includes a first testing module, a second testing module, a second acquisition module, and a second processing module. The first testing module is used to sequentially input the aforementioned target control parameter sets into the converter control simulation system for simulation testing. The second testing module is used to set the test scenario as the aforementioned target scenario and sequentially perform simulation tests on the aforementioned target control parameter sets under the aforementioned target scenario. The second acquisition module is used to acquire the key simulation waveform data obtained from the simulation tests of the aforementioned target control parameter sets, with each target control parameter set and the aforementioned key simulation waveform data corresponding one-to-one. The second processing module is used to process the aforementioned key simulation waveform data to obtain the performance index data corresponding to each of the aforementioned target control parameter sets. By analyzing the performance index data, the optimal control parameter set can be identified, thereby optimizing system performance and enabling rapid simulation testing of different parameter combinations, thus improving testing efficiency.

[0115] In some examples, the third processing unit includes a third processing module. This module processes the active power step response waveform file and the reactive power step response waveform file of the converter to obtain the rise time of the active power, the cumulative integral error of the active power, the percentage overshoot of the active power, and the rise time of the reactive power, the cumulative integral error of the reactive power, and the percentage overshoot of the reactive power, thereby obtaining the aforementioned performance index data. By processing the waveform files, the performance index data of the converter can be obtained directly without manual calculation and analysis, reducing the possibility of human error and improving the accuracy and efficiency of data processing.

[0116] In some examples, the fourth processing unit includes a third determining module, a fourth determining module, and a fourth processing module. The third determining module is used to determine the dimension of the data to be fitted based on the number of control parameters in one of the aforementioned target control parameter sets, and to determine the amount of training data based on the data in all of the aforementioned target control parameter sets. The fourth determining module is used to determine the structure of the neural network to be trained based on the dimension of the data to be fitted and the amount of training data. The structure of the neural network to be trained includes at least the number of neural network layers and the number of neurons in each layer. The fourth processing module is used to train and fit the neural network to be trained by taking the control parameters in all of the aforementioned target control parameter sets as input and the performance index data corresponding to each of the aforementioned target control parameter sets as output, thereby obtaining the aforementioned digital twin model. By fitting complex multi-parameter systems using neural network models, the prediction and optimization of system behavior can be achieved. The digital twin model obtained through training can quickly and accurately simulate and analyze the system, helping to optimize system control parameters.

[0117] In some examples, the first processing unit includes a first control module, which is used to set the PI parameters for the voltage loop and the current loop based on dual-loop control, where P is the proportional coefficient and I is the integral coefficient. By properly adjusting the PI parameters, the system response speed can be made faster, achieving a quicker control response, and effectively reducing system overshoot and oscillation, thereby improving system stability.

[0118] In some examples, the aforementioned device further includes a third acquisition module, a fifth processing module, and a fourth determination module. The third acquisition module, after training the neural network to be trained using the aforementioned target control parameter set and the corresponding performance index data to obtain a digital twin model, acquires the actual control parameters of the converter at the current moment. These actual control parameters are the control parameters used by the converter during actual operation at the current moment. The fifth processing module inputs the actual control parameters into the digital twin model to obtain the actual performance index of the converter. These actual performance indexes are performance data characterizing the actual operating state of the converter at the current moment. The fourth determination module determines the current operating state of the converter based on its actual performance index. The digital twin model simulates and analyzes the actual control parameters of the converter, which can more accurately characterize the actual operating state of the converter, improve the accuracy of judgment, and significantly reduce the required time cost while ensuring accuracy. This provides an important reference and powerful tool for the design of converter control systems in future power systems with increasingly higher levels of power electronics.

[0119] The device for determining the performance evaluation model of the aforementioned converter includes a processor and a memory. The aforementioned first processing unit, etc., are all stored as program units in the memory, and the processor executes these program units stored in the memory to achieve the corresponding functions. All of the aforementioned modules are located in the same processor; alternatively, the aforementioned modules may be located in different processors in any combination.

[0120] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured. Adjusting kernel parameters addresses the problem that existing methods for determining converter control parameters require extensive simulations or hardware experiments, which are time-consuming and inefficient.

[0121] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0122] This invention provides a computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to execute a method for determining the performance evaluation model of the converter.

[0123] Specifically, the methods for determining the performance evaluation model for converters include:

[0124] Step S201: Configure the executable script file;

[0125] Specifically, an executable file refers to a script file in real-time simulation software (RTDS) for power systems. It is implemented using a C-like language and is a script-based file.

[0126] Step S202: Execute the above-mentioned executable script file to generate at least one set of target control parameters, the target control parameter set including multiple control parameters of the above-mentioned converter, the control parameters being parameters for controlling the operation of the converter;

[0127] Specifically, generating multiple sets of target control parameters can help optimize converter operation, improve its efficiency and performance, better understand the converter's operating mechanism, and enhance the ability to control and adjust its parameters.

[0128] Step S203: Perform simulation tests on each of the above target control parameter sets under the target scenario to obtain the corresponding performance index data. The above performance index data is data that characterizes the operating status of the converter under the above target scenario when the above control parameters in the above target control parameter set are used.

[0129] Specifically, simulation testing can quickly acquire a large amount of data, which can then be analyzed and compared to quickly find the optimal control parameters and improve the performance and efficiency of the converter.

[0130] Step S204: The target control parameter set and the corresponding performance index data are used to train the neural network to obtain a digital twin model. The digital twin model is used to evaluate the performance index of the converter.

[0131] Specifically, digital twin models can be trained on real-world data and are able to predict converter performance metrics more accurately. Continuous model optimization can further improve the accuracy of evaluation results.

[0132] This invention provides a processor for running a program, wherein the program executes a method for determining the performance evaluation model of the converter.

[0133] Specifically, the methods for determining the performance evaluation model for converters include:

[0134] Step S201: Configure the executable script file;

[0135] Specifically, an executable file refers to a script file in real-time simulation software (RTDS) for power systems. It is implemented using a C-like language and is a script-based file.

[0136] Step S202: Execute the above-mentioned executable script file to generate at least one set of target control parameters, the target control parameter set including multiple control parameters of the above-mentioned converter, the control parameters being parameters for controlling the operation of the converter;

[0137] Specifically, generating multiple sets of target control parameters can help optimize converter operation, improve its efficiency and performance, better understand the converter's operating mechanism, and enhance the ability to control and adjust its parameters.

[0138] Step S203: Perform simulation tests on each of the above target control parameter sets under the target scenario to obtain the corresponding performance index data. The above performance index data is data that characterizes the operating status of the converter under the above target scenario when the above control parameters in the above target control parameter set are used.

[0139] Specifically, simulation testing can quickly acquire a large amount of data, which can then be analyzed and compared to quickly find the optimal control parameters and improve the performance and efficiency of the converter.

[0140] Step S204: The target control parameter set and the corresponding performance index data are used to train the neural network to obtain a digital twin model. The digital twin model is used to evaluate the performance index of the converter.

[0141] Specifically, digital twin models can be trained on real-world data and are able to predict converter performance metrics more accurately. Continuous model optimization can further improve the accuracy of evaluation results.

[0142] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs at least the following steps:

[0143] Step S201: Configure the executable script file;

[0144] Specifically, an executable file refers to a script file in real-time simulation software (RTDS) for power systems. It is implemented using a C-like language and is a script-based file.

[0145] Step S202: Execute the above-mentioned executable script file to generate at least one set of target control parameters, the target control parameter set including multiple control parameters of the above-mentioned converter, the control parameters being parameters for controlling the operation of the converter;

[0146] Specifically, generating multiple sets of target control parameters can help optimize converter operation, improve its efficiency and performance, better understand the converter's operating mechanism, and enhance the ability to control and adjust its parameters.

[0147] Step S203: Perform simulation tests on each of the above target control parameter sets under the target scenario to obtain the corresponding performance index data. The above performance index data is data that characterizes the operating status of the converter under the above target scenario when the above control parameters in the above target control parameter set are used.

[0148] Specifically, simulation testing can quickly acquire a large amount of data, which can then be analyzed and compared to quickly find the optimal control parameters and improve the performance and efficiency of the converter.

[0149] Step S204: The target control parameter set and the corresponding performance index data are used to train the neural network to obtain a digital twin model. The digital twin model is used to evaluate the performance index of the converter.

[0150] Specifically, digital twin models can be trained on real-world data and are able to predict converter performance metrics more accurately. Continuous model optimization can further improve the accuracy of evaluation results.

[0151] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0152] This application also provides a computer program product that, when executed on a data processing device, is suitable for executing an initialization program having at least the following method steps: step S201, configuring an executable script file;

[0153] Specifically, an executable file refers to a script file in real-time simulation software (RTDS) for power systems. It is implemented using a C-like language and is a script-based file.

[0154] Step S202: Execute the above-mentioned executable script file to generate at least one set of target control parameters, the target control parameter set including multiple control parameters of the above-mentioned converter, the control parameters being parameters for controlling the operation of the converter;

[0155] Specifically, generating multiple sets of target control parameters can help optimize converter operation, improve its efficiency and performance, better understand the converter's operating mechanism, and enhance the ability to control and adjust its parameters.

[0156] Step S203: Perform simulation tests on each of the above target control parameter sets under the target scenario to obtain the corresponding performance index data. The above performance index data is data that characterizes the operating status of the converter under the above target scenario when the above control parameters in the above target control parameter set are used.

[0157] Specifically, simulation testing can quickly acquire a large amount of data, which can then be analyzed and compared to quickly find the optimal control parameters and improve the performance and efficiency of the converter.

[0158] Step S204: The target control parameter set and the corresponding performance index data are used to train the neural network to obtain a digital twin model. The digital twin model is used to evaluate the performance index of the converter.

[0159] Specifically, digital twin models can be trained on real-world data and are able to predict converter performance metrics more accurately. Continuous model optimization can further improve the accuracy of evaluation results.

[0160] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0161] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0162] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0163] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0164] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0165] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0166] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0167] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0168] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0169] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0170] 1) The method for determining the performance evaluation model of the converter described in this application firstly configures an executable script file; then executes the executable script file to generate at least one set of target control parameters, which includes multiple control parameters of the converter, and the control parameters are parameters for controlling the operation of the converter; then, simulation tests are performed on each target control parameter set under the target scenario to obtain the corresponding performance index data, which is data characterizing the operating state of the converter under the target scenario using the control parameters in the target control parameter set; finally, the target control parameter set and the corresponding performance index data are used to train the neural network to obtain a digital twin model, which is used to evaluate the performance index of the converter. This method improves the construction of the relationship between converter control parameters and performance indicators, quantitatively describing the impact of converter control parameters on performance indicators with short processing time and high accuracy. This provides guidance and basis for the design of converter control systems. Compared with the traditional exhaustive control parameter performance evaluation method based on a large number of software simulations / experimental tests, it can significantly reduce the required time cost while ensuring accuracy. It can be used for the design of converter control systems in new power systems with a high degree of power electronics, solving the problem that existing methods for determining converter control parameters require a large number of simulations or hardware experiments, which are time-consuming and inefficient.

[0171] 2) The apparatus for determining the performance evaluation model of the converter described above in this application includes: a first processing unit, a second processing unit, a third processing unit, and a fourth processing unit. The first processing unit is used to configure an executable script file, which generates control parameters for the converter. The control parameters are parameters used to control the operation of the converter. The second processing unit is used to execute the executable script file to generate at least one set of target control parameters, which includes multiple control parameters. The third processing unit is used to perform simulation tests on each target control parameter set under a target scenario to obtain corresponding performance index data. The performance index data is data characterizing the operating state of the converter under the target scenario using the control parameters in the target control parameter set. The fourth processing unit is used to train the neural network to be trained using the target control parameter set and the corresponding performance index data to obtain a digital twin model. The digital twin model is used to evaluate the performance index of the converter. This device improves the method for constructing the relationship between converter control parameters and performance indicators, quantitatively describing the impact of converter control parameters on performance indicators with short processing time and high accuracy. This provides guidance and basis for the design of converter control systems. Compared with the traditional exhaustive control parameter performance evaluation method based on a large number of software simulations / experimental tests, it can significantly reduce the required time cost while ensuring accuracy. It can be used for the design of converter control systems in new power systems with a high degree of power electronics, solving the problem that existing methods for determining converter control parameters require a large number of simulations or hardware experimental tests, which are time-consuming and inefficient.

[0172] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for determining the performance evaluation model of a converter, characterized in that, include: Configure the executable script file; The executable script file is executed to generate at least one set of target control parameters, the set of target control parameters including multiple control parameters of the converter, the control parameters being parameters for controlling the operation of the converter; Simulation tests are performed on each of the target control parameter sets under the target scenario to obtain the corresponding performance index data. The performance index data is data that characterizes the operating status of the converter when running with the control parameters in the target control parameter set under the target scenario. The target control parameter set and the corresponding performance index data are used to train the neural network to obtain a digital twin model. This digital twin model is used to evaluate the performance index of the converter. The process involves performing simulation tests on each set of target control parameters under a target scenario to obtain corresponding performance index data. This includes: sequentially inputting each set of target control parameters into a converter control simulation system for simulation testing; setting the test scenario as the target scenario and sequentially performing simulation tests on each set of target control parameters under the target scenario; acquiring key simulation waveform data obtained from the simulation tests on each set of target control parameters, where each set of target control parameters corresponds one-to-one with the key simulation waveform data; and processing the key simulation waveform data to obtain performance index data corresponding to each set of target control parameters. The key simulation waveform data includes the active power step response waveform file and the reactive power step response waveform file of the converter. The key simulation waveform data is processed to obtain the performance index data corresponding to each set of target control parameters. This includes processing the active power step response waveform file and the reactive power step response waveform file of the converter to obtain the rise time of the active power, the cumulative integral error of the active power, the percentage overshoot of the active power, and the rise time of the reactive power, the cumulative integral error of the reactive power, and the percentage overshoot of the reactive power, so as to obtain the performance index data.

2. The method according to claim 1, characterized in that, Executing the executable script file to generate at least one set of target control parameters includes: Obtain a preset parameter scale, which includes: a preset number of control parameter sets and a preset value range for each control parameter; The executable script file is executed based on the preset parameter scale to generate an initial control parameter set, wherein the values ​​of the control parameters in the initial control parameter set are all within the corresponding preset value range. Determine whether the number of generated initial control parameter sets has reached the preset number; If the number of generated initial control parameter sets reaches the preset number, all generated initial control parameter sets are determined as the target control parameter set; If the number of generated initial control parameter sets has not reached the preset number, the executable script file will continue to be executed to generate the initial control parameter sets again, based on the preset parameter size and the principle of independent and identical distribution.

3. The method according to claim 1, characterized in that, The target control parameter set and the corresponding performance index data are used to train the neural network to obtain a digital twin model, including: The dimension of the data to be fitted is determined based on the number of control parameters in one of the target control parameter sets, and the amount of training data is determined based on the data in all the target control parameter sets. Based on the dimension of the data to be fitted and the amount of training data, the structure of the neural network to be trained is determined. The structure of the neural network to be trained includes at least the number of neural network layers and the number of neurons in each neural network layer. The control parameters in all the target control parameter sets are used as inputs, and the performance index data corresponding to each target control parameter set are used as outputs to train and fit the neural network to obtain the digital twin model.

4. The method according to claim 1, characterized in that, The control parameters of the converter include: PI parameters of the voltage loop based on dual-loop control and PI parameters of the current loop based on dual-loop control, where P is the proportional coefficient and I is the integral coefficient.

5. The method according to claim 1, characterized in that, After training the neural network to be trained using the target control parameter set and the corresponding performance index data to obtain a digital twin model, the method further includes: Obtain the actual control parameters of the converter at the current moment, wherein the actual control parameters are the control parameters used by the converter during actual operation at the current moment; The actual control parameters are input into the digital twin model to obtain the actual performance index of the converter. The actual performance index is the performance data that characterizes the actual operating state of the converter at the current moment. The current operating status of the converter is determined based on its actual performance indicators.

6. A device for determining an index evaluation model for a converter, characterized in that, include: The first processing unit is used to configure an executable script file, which is used to generate control parameters for the converter, and the control parameters are parameters for controlling the operation of the converter. The second processing unit is configured to execute the executable script file to generate at least one set of target control parameters, wherein the set of target control parameters includes multiple control parameters; The third processing unit is used to perform simulation tests on each of the target control parameter sets under the target scenario to obtain corresponding performance index data. The performance index data is data that characterizes the operating status of the converter when running with the control parameters in the target control parameter set under the target scenario. The fourth processing unit is used to train the neural network to be trained using the target control parameter set and the corresponding performance index data to obtain a digital twin model. The digital twin model is used to evaluate the performance index of the converter. The third processing unit includes a first testing module, a second testing module, a second acquisition module, and a second processing module. The first testing module is used to sequentially input each of the target control parameter sets into the converter control simulation system for simulation testing. The second testing module is used to set the test scenario as the target scenario and sequentially perform simulation testing on each of the target control parameter sets under the target scenario. The second acquisition module is used to acquire key simulation waveform data obtained by simulation testing of each of the target control parameter sets, wherein the target control parameter sets and the key simulation waveform data correspond one-to-one; the second processing module is used to process the key simulation waveform data to obtain performance index data corresponding to each of the target control parameter sets. The third processing unit includes a third processing module, which processes the active power step response waveform file and the reactive power step response waveform file of the converter to obtain the rise time of the active power, the cumulative integral error of the active power, the percentage overshoot of the active power, the rise time of the reactive power, the cumulative integral error of the reactive power, and the percentage overshoot of the reactive power, so as to obtain the performance index data.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the method for determining the performance evaluation model of the converter as described in any one of claims 1 to 5.

8. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including a method for determining an index evaluation model for a converter according to any one of claims 1 to 5.

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