Remote testing system of power grid equipment and remote testing method of power grid equipment

Abstract

The application relates to a remote test system of power grid equipment and a remote test method of power grid equipment. The remote test system comprises at least one remote device to be tested, a digital twin system, a remote test control system and a full-scale test device. The remote test control system is used for acquiring test information of the device to be tested input by a user. The digital twin system is used for generating a target test scene according to the test information, test excitation data of the full-scale test device under standard working conditions and device response data of the device to be tested under the test excitation data. The remote test control system is further used for testing the device to be tested according to the target test scene to obtain test results. The remote test system of the power grid equipment can realize high-fidelity, deterministic, real-time, safe and interoperable remote testing of distribution network equipment.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a remote testing system and method for power grid equipment. Background Technology

[0002] The power distribution network is undergoing profound changes, with a large number of new power devices such as distributed power sources, energy storage systems, and electric vehicles being connected to the grid, making their operating characteristics increasingly complex. To ensure the compatibility of these new power devices with the grid and the safety and stability of the system, rigorous testing and verification of the power equipment are essential.

[0003] However, traditional power grid equipment testing methods suffer from low testing efficiency. Summary of the Invention

[0004] Therefore, it is necessary to provide a remote testing system and method for power grid equipment that can improve testing efficiency in response to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a remote testing system for power grid equipment, the remote testing system comprising: at least one remote device under test, a digital twin system, a remote test control system, and a full-scale test device; the remote test control system is connected to the digital twin system, the device under test, and the full-scale test device, and the digital twin system is connected to the device under test and the full-scale test device.

[0006] The remote test and control system is used to acquire test information of the device under test (DUT) input by the user; the test information includes test tasks, attribute information, and the initial simulation model corresponding to the DUT.

[0007] A digital twin system is used to generate a target test scenario based on test information, test stimulus data of a real-world test device under standard operating conditions, and device response data of the device under test under the test stimulus data.

[0008] The remote test and control system is also used to test the device under test according to the target test scenario and obtain test results.

[0009] In some embodiments, the remote test control system includes: an integration middleware and a test management module; the integration middleware is connected to the test management module;

[0010] Integrated middleware is used to obtain test information of the device under test input by the user;

[0011] The test management module is used to call the digital twin system to generate target test scenarios, and to test the device under test according to the target test scenarios to obtain test results.

[0012] In some embodiments, the integration middleware includes: a model building unit and a protocol adaptation unit; the model building unit is connected to the protocol adaptation unit;

[0013] The model building unit is used to create a standardized information model for the device under test based on its attribute information when the device under test is connected.

[0014] The protocol adapter unit is used to enable the device under test to interact with other devices.

[0015] In some embodiments, the test management module includes: multiple instruction generation units and test units;

[0016] The instruction generation unit is used to generate synchronized start instructions for the prototype test equipment, the device under test, and the digital twin system to start the prototype test equipment, the device under test, and the digital twin system;

[0017] The test unit is used to compare whether the actual measurement data output by the device under test is consistent with the simulation prediction data output by the digital twin system. If they are consistent, the test result is determined to be a pass; if they are inconsistent, the test result is determined to be a fail.

[0018] In some embodiments, the plurality of instruction generation units include: a first instruction generation unit, a second instruction generation unit, and a third instruction generation unit;

[0019] The first instruction generation unit is used to generate a first instruction to control the real-type test equipment to reproduce the power grid simulation signal corresponding to the target test scenario;

[0020] The second instruction generation unit is used to generate a second instruction to control the device under test to operate under the power grid simulation signal and output actual measurement data.

[0021] The third instruction generation unit is used to generate a third instruction to control the digital twin system to perform tests based on the simulation signal and output simulation prediction data; the simulation signal corresponds to the power grid simulation signal.

[0022] In some embodiments, the digital twin system includes: a calibration module and a simulation exercise module; the calibration module is connected to the simulation exercise module;

[0023] The calibration module is used to calibrate the initial simulation model based on the test stimulus data and the equipment response data to obtain a high-fidelity digital twin model.

[0024] The simulation exercise module is used to conduct various test scenario exercises based on the test task to obtain the target test scenario.

[0025] In some embodiments, the correction module includes: a simulation unit and a correction unit;

[0026] The simulation unit is used to input test stimulus data into the initial simulation model to perform simulation tests and obtain simulation results.

[0027] The calibration unit is used to optimize the parameters of the initial simulation model based on the simulation results and equipment response data to obtain a high-fidelity digital twin model.

[0028] In some embodiments, the digital twin system further includes an optimization module;

[0029] The optimization module is used to extract extreme scenarios from various test scenarios and optimize the initial simulation model or high-fidelity digital twin model based on the extreme scenarios.

[0030] In some embodiments, the remote testing system further includes a communication system, which includes a time synchronization module and a data transmission module;

[0031] The time synchronization module is used to perform time synchronization processing on all access devices;

[0032] The data transmission module is used to identify, schedule, and shape the received data according to a preset traffic priority strategy.

[0033] The remote test and control system is connected to the digital twin system, the device under test (DUT), and the prototype test equipment via a data transmission module; the digital twin system is connected to the DUT and the prototype test equipment via a data transmission module; and the time synchronization module is connected to the clock nodes in the DUT, the digital twin system, the remote test and control system, and the prototype test equipment via a data transmission module.

[0034] Secondly, this application also provides a remote testing method for power grid equipment, which is applied to a remote testing system for power grid equipment as described in any of the first aspects, and the method includes:

[0035] The remote test and control system acquires test information of the device under test (DUT) input by the user; the test information includes test tasks, attribute information, and the initial simulation model corresponding to the DUT;

[0036] The digital twin system generates a target test scenario based on test information, test stimulus data of the real test equipment under standard operating conditions, and equipment response data of the device under test under the test stimulus data.

[0037] The remote test control system performs tests on the device under test according to the target test scenario and obtains the test results.

[0038] The aforementioned remote testing system and method for power grid equipment include at least one remote device under test (DUT), a digital twin system, a remote test control system, and a full-scale test device. The remote test control system acquires test information input by the user for the DUT. The digital twin system generates a target test scenario based on the test information, test stimulus data from the full-scale test device under standard operating conditions, and the device response data of the DUT under the test stimulus data. The remote test control system also performs tests on the DUT according to the target test scenario to obtain test results. The remote test control system connects to the digital twin system, the DUT, and the full-scale test device, and the digital twin system connects to both the DUT and the full-scale test device. The test information includes test tasks, attribute information, and the initial simulation model corresponding to the DUT. By testing the remote DUT, the geographical limitations of traditional testing are overcome, enabling in-situ testing of the equipment and eliminating the need to transport large or fixed power grid equipment to a specific test site, significantly reducing the complexity and cost of testing. The digital twin system generates target test scenarios using test stimulus data and equipment response data, ensuring that the test scenarios originate from the operational data of the actual physical system, rather than being purely theoretical derivations. Therefore, it can more effectively expose potential problems of equipment under real or extreme operating conditions, significantly improving the relevance and depth of the testing. By deeply integrating the digital twin system with real-world test equipment and the device under test, a closed loop of "physical data driving virtual modeling, and virtual scenarios guiding physical testing" is formed. This overcomes the respective shortcomings of high cost and low flexibility in pure physical testing and low reliability in pure digital simulation, combining their advantages. Planning and optimization are performed in the digital space, followed by execution and verification in the physical space, thereby enhancing the scientific rigor and reliability of the entire testing process. Therefore, this remote testing system for power grid equipment can achieve high-fidelity, deterministic, real-time, secure, and interoperable remote testing of distribution network equipment. Attached Figure Description

[0039] Figure 1 This is one of the structural schematic diagrams of a remote testing system for power grid equipment in some embodiments;

[0040] Figure 2 This is a schematic diagram of the structure of the remote test control system in some embodiments;

[0041] Figure 3 This is one of the structural schematic diagrams of a digital twin system in some embodiments;

[0042] Figure 4 This is a second schematic diagram of the structure of a digital twin system in some embodiments;

[0043] Figure 5 This is a second schematic diagram of the structure of a remote testing system for power grid equipment in some embodiments;

[0044] Figure 6 These are schematic diagrams of the structure of a full-scale experimental digital twin platform system in some embodiments;

[0045] Figure 7 This is a flowchart illustrating a physical system-based testing method in some embodiments;

[0046] Figure 8 This is a schematic diagram illustrating the generation of digital twin models in some embodiments;

[0047] Figure 9 This is a flowchart illustrating a remote testing method for power grid equipment in some embodiments. Detailed Implementation

[0048] In the embodiments of this application, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0049] In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0050] In the embodiments of this application, the term "at least one" means one or more. For example, at least one of A, B and C can represent six situations: A exists alone, B exists alone, C exists alone, A and B exist simultaneously, A and C exist simultaneously, B and C exist simultaneously, and A, B and C exist simultaneously.

[0051] 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 a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0052] The power distribution network is undergoing profound changes, with a large number of new devices such as distributed power sources, energy storage systems, and electric vehicles being connected to the grid, making its operating characteristics increasingly complex. To ensure the compatibility of these new devices with the power grid and the safety and stability of the system, rigorous testing and verification are essential. However, existing testing technologies have significant limitations in addressing this challenge. Traditional physical tests replicate power grid equipment and topology for testing. While offering high fidelity, their inherent limitations restrict their application scope and efficiency. Real-world test sites require substantial investment, and the testing process heavily relies on manual operation, resulting in low efficiency, lack of flexibility and scalability. Once established, the topology and equipment configuration are difficult to change in a short period, and upgrading the physical platform is costly and time-consuming in the face of constantly emerging new devices and changing operating scenarios. Pure digital simulation or digital twin technologies, as supplements to physical testing, offer advantages in cost and flexibility, but their model accuracy issues make them unsuitable as the final verification method. To combine the advantages of both, some preliminary integration schemes have emerged, but these schemes have a shallow degree of integration and have failed to form an organic whole. The current mainstream approach to digital twin integration is unidirectional, meaning that physical test data is used for offline calibration of digital models. Digital twin models cannot guide or control the process of physical experiments in real-time or in a closed-loop manner. The model correction process involves manual analysis and data import, taking hours or even days, and cannot adapt to the dynamic changes in the physical equipment's state. In summary, existing technologies exhibit a mutually reinforcing failure cycle: the high cost and low flexibility of physical testing drive the demand for digital simulation; however, the fidelity gap in digital simulation necessitates reliance on physical testing for verification, thus again falling into a cost and efficiency dilemma, resulting in low testing efficiency in traditional power grid equipment testing methods.

[0053] In view of this, embodiments of this application propose a remote testing system and a remote testing method for power grid equipment. The system can support high-fidelity, real-time, and secure remote hardware-in-the-loop testing of power equipment, thereby improving testing efficiency.

[0054] It should be noted that the beneficial effects or technical problems solved by the embodiments of this application are not limited to this one, but may also be other implicit or related problems. For details, please refer to the description of the embodiments below.

[0055] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0056] In some embodiments, such as Figure 1As shown, a remote testing system for power grid equipment is provided, the remote testing system comprising: at least one remote device under test 10 ( Figure 1 (Taking a device under test as an example for illustration), the system consists of a digital twin system 20, a remote test control system 30, and a full-scale test device 40. The remote test control system is connected to the digital twin system, the device under test, and the full-scale test device, respectively. The digital twin system is also connected to both the device under test and the full-scale test device.

[0057] The remote test and control system is used to acquire test information from the user-inputted device under test (DUT). This test information includes the test task, attribute information, and the initial simulation model corresponding to the DUT. The test task refers to the test objective and content, such as "overload capacity verification" or "fault ride-through test." Attribute information includes the DUT's network address and communication protocol. The initial simulation model is the simulation model corresponding to the DUT from the simulation model library. It is a basic simulation model that has not undergone optimization or calibration. The structure and basic parameters of the basic simulation model match the physical characteristics and operating laws of the power grid equipment, and include multi-physics coupled models such as electromagnetic, thermal, and mechanical models.

[0058] The digital twin system generates a target test scenario based on test information, test stimulus data from a real-world test device under standard operating conditions, and device response data from the device under test (DUT) under the test stimulus data. Standard operating conditions refer to the normal operating state of the DUT under rated parameters, including baseline operating modes such as no-load operation and rated load operation. Test stimulus data, generated by the real-world test device, corresponds to the external electrical or environmental conditions of the standard operating conditions and is applied to the DUT, such as harmonic waveforms. Device response data, generated by the remote DUT, describes the operating state and behavior of the DUT under the stimulus, such as real-time data on parameters like voltage, power, and temperature. The target test scenario can be a standard test scenario directly input by the user, or a test scenario autonomously explored and generated by the digital twin system through multiple simulations.

[0059] The remote test and control system is also used to test the device under test according to the target test scenario and obtain test results. The test results include whether the test passes or fails. A passing test indicates that the device under test meets the grid connection standards, while a failing test indicates that the device under test does not meet the grid connection standards.

[0060] The aforementioned full-scale testing equipment is located in a local, fixed, and well-equipped central laboratory (e.g., a laboratory at the China Electric Power Research Institute in Beijing). This laboratory possesses infrastructure such as precise and controllable power supply and load simulators. The full-scale testing equipment is a high-precision physical device used to simulate the actual operating environment of the power grid; it is not the equipment actually operating in the power grid, but rather a controlled "environment simulator" specifically designed for testing.

[0061] The aforementioned device under test (DUT) is located in a remote location, which can be any site requiring testing. Examples include a wind power converter in a wind farm in Hebei, an electric vehicle charging station in a residential community in Shanghai, and a photovoltaic inverter in a factory in Guangdong. There can be one or multiple DUTs. Key nodes of the DUT are equipped with high-precision, high-frequency multimodal sensors, such as voltage sensors, current sensors, temperature sensors, partial discharge sensors, and mechanical vibration sensors.

[0062] The aforementioned digital twin systems are typically located at the central end, deployed in a data center or cloud server. To achieve the lowest communication latency and highest collaborative efficiency, they are preferably deployed in the same campus or data center as the local prototype testing equipment, connected via a high-speed local area network. Alternatively, they can be deployed in the cloud.

[0063] The aforementioned remote test and control system can be deployed anywhere with a network connection. The system includes a user interface. It can be deployed in a central laboratory for easy administrator operation, or it can be a web service allowing authorized users (such as engineers from equipment manufacturers) to log in from anywhere via the internet (e.g., an office in Shenzhen), create test tasks, and monitor the testing process.

[0064] Within the central laboratory, a local TSN network can be used to connect live-action test equipment, sensors, edge computing nodes, and servers of the digital twin system. A wide area network (WAN) can be used to connect the central laboratory with various remote devices under test. To achieve deterministic low latency, dedicated lines from carriers can be leased, or cutting-edge technologies such as TSN over IP can be used on the WAN to ensure service quality.

[0065] In this embodiment, when testing a remote device under test (DUT), the user can create a test task on the user interface of the remote test control system, input the DUT's attribute information, and select an initial simulation model corresponding to the DUT from the model library. Optionally, the remote test control system can automatically match the initial simulation model corresponding to the DUT from the model library based on the DUT's attribute information. If the DUT is accessing the remote test system for the first time, the remote test control system can parse the DUT's communication protocol manual and register a digital identity for the DUT after obtaining the DUT's test information. If the DUT is not accessing the remote test system for the first time, the remote test control system can directly retrieve previously common digital identities from the database and obtain test-related information.

[0066] After acquiring the initial simulation model, the remote test and control system can load it into the digital twin system and send the test information of the device under test (DUT) to the digital twin system. Then, the remote test and control system issues "standard operating condition test" commands to both the prototype test equipment and the DUT. Upon receiving the command, the prototype test equipment reproduces the standard operating condition, generates test stimulus data, and applies the test stimulus data to the DUT. The DUT then responds to the test stimulus data, outputting device response data, and finally transmits the test stimulus data and device response data to the digital twin system. Optionally, sensing and edge processing modules can be installed on the prototype test equipment and the test equipment to acquire data, perform preprocessing and standardization operations, and then transmit the processed data to the digital twin system. After obtaining the test information, test stimulus data, and device response data, the digital twin system can optimize the initial simulation model based on these data, generate a target test scenario using the optimized simulation model, and send the target test scenario to the remote test and control system.

[0067] After receiving the target test scenario, the remote test and control system can generate test instructions based on the target scenario, perform tests on the device under test, determine whether the device under test has passed the test based on whether the output results of the device under test are consistent with the standard results, obtain the test results, and generate a test report based on the test results and display it on the user interface.

[0068] For example, consider a specific scenario: A Shanghai-based equipment manufacturer needs to test the grid adaptability of its wind power converters installed at a wind farm in Hebei Province. The testing process includes the following stages:

[0069] In the first phase, task initiation and system access: Equipment engineers in Shanghai log into the remote test and control system's web portal via a browser. In the test interface, they trigger the "Create Test Task" function to create a new test task, such as "Test the converter's fault ride-through capability under grid voltage dips," and input the network address and communication protocol (e.g., Modbus TCP) of the converter in the wind farm under test in Hebei. The remote test and control system (potentially deployed in a central cloud or Beijing laboratory) receives the instruction, automatically parses the converter's protocol, and creates an OPC UA information model for it. At this moment, this physical device in Hebei possesses a standard "digital identity" in the digital world.

[0070] The second phase involves model loading and initial synchronization: Equipment engineers can load "Converter A Model v2.0" into the digital twin system in Beijing by entering it in the "Simulation Model" drop-down menu or search box on the user interface. The digital twin system starts the model and requests initial data (i.e., test stimulus data and equipment response data) from the converter in Hebei and the local laboratory equipment in Beijing via the communication system. The digital twin system performs rapid online calibration, quickly synchronizing the model with the real converter state (error <2%). At this point, a preliminary "digital-physical" closed loop spanning Beijing and Hebei has been established.

[0071] The third phase, intelligent scenario generation and safety pre-simulation (digital sandbox simulation): The reinforcement learning agent built into the digital twin system is activated. Within the Beijing digital twin system, it performs millions of simulated impacts on the previously calibrated converter model, autonomously exploring and generating a complex voltage drop scenario with specific phases and depths that most effectively tests controller performance. It's important to note that, regardless of the approach, the generated test scenario is first and only run at full speed within the digital twin system. The current, voltage, and temperature parameters of the virtual converter under impact are monitored to ensure no equipment damage risk. This phase is entirely completed within the Beijing digital twin system, without involving any physical equipment, realizing the safety concept of "testing in the virtual world and executing in the real world."

[0072] The fourth stage involves synchronized virtual and real-world testing: The remote test control system generates test commands based on the target scenario and simultaneously sends synchronous start commands to three execution terminals. It controls the power grid simulator (real-scale test equipment) in the Beijing laboratory to reproduce the pre-simulated voltage drop waveform, controls the converter under test (DUT) in the Hebei wind farm to enter the test state, and controls the calibrated converter model (optimized initial simulation model) to start simulation synchronously with the physical test. At this moment, the Beijing power grid simulator generates a realistic voltage disturbance, which is applied to the Hebei converter through the power grid (or power amplifier). Simultaneously, the digital twin system performs a completely consistent simulation in virtual space. Finally, based on the output results of the DUT and the output results of the converter model, it is determined whether the DUT is qualified, and the test results are obtained.

[0073] The remote testing system for power grid equipment provided in this application overcomes the geographical limitations of traditional testing by testing remotely to the device under test (DUT). It enables in-situ testing of the equipment, eliminating the need to transport large or fixed power grid equipment to a specific test site, thus significantly reducing the complexity and cost of testing. The digital twin system generates target test scenarios using test stimulus data and equipment response data, ensuring that the test scenarios originate from the operational data of the actual physical system, rather than purely theoretical derivations. Therefore, it can more effectively expose potential problems of the equipment under real or extreme operating conditions, significantly improving the relevance and depth of the testing. By deeply integrating the digital twin system with real-world test equipment and the DUT, a closed loop of "physical data driving virtual modeling, and virtual scenarios guiding physical testing" is formed. This overcomes the respective shortcomings of high cost and low flexibility in pure physical testing and low reliability in pure digital simulation, combining their advantages. Planning and optimization are performed in digital space, followed by execution and verification in physical space, thereby enhancing the scientific rigor and reliability of the entire testing process. Therefore, this remote testing system for power grid equipment can achieve high-fidelity, deterministic, real-time, secure, and interoperable remote testing of distribution network equipment.

[0074] In some embodiments, such as Figure 2 As shown, the aforementioned remote test and control system includes: an integration middleware 301 and a test management module 302. The test management module is connected to the integration middleware.

[0075] The integrated middleware is used to acquire test information of the device under test (DUT) input by the user, serving as the input interface for the remote test and control system. The test management module is used to call the digital twin system to generate target test scenarios, and then perform tests on the DUT according to these scenarios to obtain test results. It serves as the control core of the remote test and control system.

[0076] In this embodiment, the user inputs test information through the human-computer interaction interface of the integrated middleware. The integrated middleware identifies the communication protocol type of the device under test (such as Modbus TCP, Profinet, etc.), automatically loads the corresponding protocol adapter, establishes a communication connection with the remote device under test, verifies the stability of the communication link, confirms the online status of the device, and then encapsulates the collected test information into a standard data format and transmits the complete test information package to the test management module through the internal interface.

[0077] The test management module parses the received test information, identifies the test objectives and constraints, and then sends a model loading command to the digital twin system, transferring the initial simulation model, coordinating the real-world test equipment to enter the preparation state, and configuring basic operating parameters. Next, it triggers the digital twin system to begin standard operating condition test data acquisition, monitors the data stream transmission status, and ensures the integrity of test stimulus data and equipment response data. Finally, it sends a scenario generation command to the digital twin system, transferring necessary test parameters, receives the target test scenario data package generated by the digital twin system, performs format verification and integrity checks on the scenario data, and prepares to enter the test execution phase.

[0078] The test management module parses the target test scenario into a specific sequence of device control commands. Through middleware integration, it sends operating mode commands to the remote device under test (DUT) and stimulus generation commands to the prototype test equipment, reproducing the required operating conditions of the test scenario. It then monitors the test execution progress to ensure that all systems work collaboratively according to the predetermined sequence, collects real-time response data from the DUT under the test scenario, monitors system operating status, and has the capability to handle interruptions in abnormal situations. Finally, it integrates all performance data collected during the test, analyzes the key performance indicators of the device under the target test scenario, generates a test result report containing quantitative analysis and qualitative evaluation, and outputs the final test conclusions and recommendations through the user interface.

[0079] In some embodiments, the aforementioned integration middleware includes a model building unit and a protocol adaptation unit.

[0080] The model building unit is connected to the protocol adaptation unit. The model building unit creates a standardized information model for the device under test (DUT) based on its attribute information when the DUT is connected. The protocol adaptation unit enables the DUT to interact with other devices.

[0081] In this embodiment, when a new device under test (DUT) is detected accessing the system, the model building unit first identifies the DUT, parses its unique attribute information, and uses this as a blueprint to dynamically create a standardized information model for the DUT. This information model acts like a "standard ID card" for the device, defining and describing all its functions and data in a unified language that the platform can understand, thereby establishing a standardized image of the device in the digital world. Subsequently, after the information model is established, the protocol adaptation unit uses this model to realize the interactive operation between the device and other components of the system. The protocol adaptation unit establishes a mapping relationship between the device's native data and the standardized information model by loading the corresponding communication protocol driver. In the data acquisition direction, the protocol adaptation unit obtains the operating data through the device's native protocol and converts it into a standard format to write into the information model; in the control command direction, it converts the standardized control commands issued by the system into native protocol commands that the device can recognize.

[0082] Through the collaborative work of the model building unit and the protocol adaptation unit, the integration middleware establishes a unified access and interaction channel between heterogeneous devices and the standardized testing platform. The model building unit is responsible for establishing the standard digital representation of the devices, while the protocol adaptation unit is responsible for implementing specific protocol conversions and data interactions. Together, they ensure that the system can access and control various heterogeneous devices in a consistent manner, providing standardized support for the device access layer of remote testing systems.

[0083] In some embodiments, the test management module described above includes multiple instruction generation units and test units.

[0084] The instruction generation unit generates synchronized start instructions for the prototype test equipment, the device under test (DUT), and the digital twin system to initiate these components. The testing unit compares the actual measurement data output by the DUT with the simulation prediction data output by the digital twin system. If they match, the test is considered passed; otherwise, it is considered failed.

[0085] In this embodiment, when the system enters the test execution phase, the instruction generation unit first generates synchronized start instructions for the prototype test equipment, the device under test (DUT), and the digital twin system. These instructions, through a precise time synchronization mechanism, ensure that the three systems can start and run under the same time reference, establishing a unified time basis for subsequent data comparison and analysis. After system startup, the test unit begins executing the core test judgment function. This unit collects actual measurement data from the DUT and simulation prediction data from the digital twin system in real time, and performs precise comparative analysis on the two sets of data. During the comparison process, the test unit determines whether the two sets of data are consistent based on a preset tolerance range. If the actual measurement data and the simulation prediction data are consistent within the allowable error range, the test result is determined to be passed; if a difference exceeding the allowable range is found, the test result is determined to be failed.

[0086] Throughout the workflow, the instruction generation unit and the testing unit form a complete test control closed loop. The instruction generation unit ensures the consistency and synchronization of the test environment, providing a reliable basis for data comparison; the testing unit, on the other hand, performs precise analysis based on synchronously acquired data to form the final test conclusions. The collaborative work of the two units enables the test management module to effectively coordinate all components of the system, ensuring the standardization of the testing process and the accuracy of the test results.

[0087] In some embodiments, the plurality of instruction generation units include: a first instruction generation unit, a second instruction generation unit, and a third instruction generation unit.

[0088] The system comprises three main components: a first instruction generation unit, a second instruction generation unit, and a third instruction generation unit. The first instruction generates a first instruction to control the real-world test equipment to reproduce the power grid simulation signal corresponding to the target test scenario. The second instruction generation unit generates a second instruction to control the device under test (DUT) to operate under the power grid simulation signal and output actual measurement data. The third instruction generation unit generates a third instruction to control the digital twin system to perform tests based on the simulation signal and output simulation prediction data; the simulation signal corresponds to the power grid simulation signal.

[0089] In this embodiment, when the test enters the execution phase, multiple instruction generation units synchronously initiate their workflows. The first instruction generation unit generates a first instruction, controlling the full-scale test equipment to accurately reproduce the power grid simulation signal corresponding to the target test scenario; the second instruction generation unit generates a second instruction, controlling the device under test to operate under the power grid simulation signal and output actual measurement data; the third instruction generation unit generates a third instruction, controlling the digital twin system to perform tests based on the simulation signal corresponding to the power grid simulation signal and output simulation prediction data. These three instruction generation units ensure coordinated operation of each system through a time synchronization mechanism, establishing a unified benchmark for data comparison.

[0090] During the test execution, the test unit simultaneously collects actual measurement data from the device under test (DUT) and simulated prediction data from the digital twin system, and performs precise comparative analysis on the two sets of data. Based on preset judgment criteria, the unit checks the consistency between the actual measurement data and the simulated prediction data. If the two sets of data are consistent within the allowable error range, the test result is considered passed; if a difference exceeding the allowable range is found, the test result is considered failed.

[0091] The test management module, through the collaborative operation of the instruction generation unit and the testing unit, achieves precise control of the testing process and automatic determination of test results. The instruction generation unit ensures the consistency and synchronization of the testing environment, providing a reliable basis for data comparison; the testing unit, on the other hand, performs precise analysis based on synchronously acquired data to form the final test conclusions. This modular working mechanism guarantees the standardization of the testing process and the accuracy of the test results, providing reliable technical support for remote testing of power grid equipment.

[0092] In some embodiments, such as Figure 3 As shown, the above-mentioned digital twin system includes: a calibration module 201 and a simulation exercise module 202; the calibration module is connected to the simulation exercise module;

[0093] The calibration module is used to calibrate the initial simulation model based on test stimulus data and equipment response data to obtain a high-fidelity digital twin model. The simulation exercise module is used to perform various test scenario exercises based on the test task to obtain the target test scenario.

[0094] In this embodiment, after the system receives the test task and related data, the calibration module first initiates its workflow. This module receives test stimulus data from the prototype test equipment and device response data from the device under test (DUT), and performs parameter calibration and model optimization on the initial simulation model based on these two types of data. Through a data-driven calibration process, the initial simulation model is upgraded to a high-fidelity digital twin model consistent with the characteristics of the physical device. After the calibration process is completed, the simulation exercise module conducts test scenario simulations based on the generated high-fidelity digital twin model. This module simulates various possible test scenarios in a digital environment according to the test task requirements, and evaluates the device performance under different scenarios through simulation calculations. After multiple rounds of simulation simulations and scenario optimization, a target test scenario that meets the test requirements is finally obtained.

[0095] The two modules work closely together to form a complete workflow: the calibration module ensures the consistency between the digital model and the physical equipment, providing an accurate digital carrier for simulation; the simulation exercise module utilizes the calibrated high-fidelity model to explore and optimize test scenarios in virtual space. This division of labor and collaboration mechanism ensures both the accuracy of the digital model and the effectiveness of the test scenarios, providing a reliable basis for subsequent physical testing. Through the collaborative work of the calibration and simulation exercise modules, the digital twin system achieves a complete transformation process from raw data to a high-fidelity model, and then to optimized test scenarios, providing an accurate digital foundation and support for remote testing of power grid equipment.

[0096] In some embodiments, the above-mentioned correction module includes: a simulation unit and a correction unit;

[0097] The simulation unit is used to input test stimulus data into the initial simulation model for simulation testing and obtain simulation results. The calibration unit is used to optimize the parameters of the initial simulation model based on the simulation results and equipment response data to obtain a high-fidelity digital twin model.

[0098] In this embodiment, after the test stimulus data and device response data are input into the calibration module, the simulation unit first initiates its workflow. This unit inputs the test stimulus data from the actual test equipment into the initial simulation model, runs the simulation test, and obtains the corresponding simulation results. Subsequently, the calibration unit optimizes the parameters of the initial simulation model based on the simulation results output by the simulation unit and the device response data from the device under test. This unit identifies model parameter deviations by comparing and analyzing the differences between the simulation results and the actual device response data, and adjusts the model parameters through optimization algorithms to make the model's output more consistent with the actual response of the physical device.

[0099] The two units collaborate sequentially to form a complete calibration process: the simulation unit is responsible for reproducing the physical testing process in digital space and generating comparable simulation results; the calibration unit is responsible for optimizing the model's parameters based on the differences between the actual data and the simulation results. This working mechanism ensures that the digital model accurately reflects the dynamic characteristics of the physical equipment, providing a high-precision digital carrier for subsequent simulation exercises. Through the collaborative work of the simulation and calibration units, the calibration module realizes the transformation from the initial simulation model to a high-fidelity digital twin model, providing a reliable model foundation for the entire digital twin system.

[0100] In some embodiments, such as Figure 4 As shown, the above-mentioned digital twin system also includes: optimization module 203.

[0101] The optimization module is used to extract extreme scenarios from various test scenarios and optimize the initial simulation model or high-fidelity digital twin model based on the extreme scenarios.

[0102] In this embodiment, based on the simulation exercise module generating various test scenarios, the optimization module analyzes and processes the scenario library to identify and extract representative extreme scenarios. These extreme scenarios typically include critical test situations such as extreme operating conditions, boundary conditions, or fault states.

[0103] The optimization module utilizes extracted extreme scenarios to perform targeted optimizations on the simulation model in the digital twin system. This optimization process can be divided into two levels: first, preprocessing optimization of the initial simulation model, which enhances the model's performance under special conditions by introducing extreme scenario data during the model training stage; second, strengthening optimization of the calibrated high-fidelity digital twin model, which further improves the model's prediction accuracy under critical conditions through verification and calibration in extreme scenarios.

[0104] The optimization module works collaboratively with other modules in the system: the simulation module provides basic scenario data, the calibration module ensures the basic accuracy of the model, and the optimization module focuses on improving the model's performance under special operating conditions. These three modules together constitute a complete technical path from basic modeling to fine-grained optimization. Through the work of the optimization module, the digital twin system can not only handle routine test scenarios but also possess special optimization capabilities to cope with extreme operating conditions. This design significantly improves the reliability and applicability of the digital twin model in practical engineering applications, providing more comprehensive technical support for the safety assessment and risk warning of power grid equipment.

[0105] In some embodiments, such as Figure 5 As shown, the remote testing system for the aforementioned power grid equipment also includes a communication system 50, which includes a time synchronization module and a data transmission module.

[0106] The time synchronization module is used to perform time synchronization processing on all access devices. The data transmission module is used to identify, schedule, and shape the received data according to a preset traffic priority strategy.

[0107] The remote test and control system connects to the digital twin system, the device under test (DUT), and the prototype test equipment via a data transmission module. The digital twin system connects to both the DUT and the prototype test equipment via the same data transmission module. The time synchronization module connects to the clock nodes in all three systems via the data transmission module. This time synchronization module establishes connections with all clock nodes in the system, providing a unified time reference for the DUT, DUT, remote test and control system, and prototype test equipment. The module uses a precision clock protocol to synchronize the entire system, ensuring that all devices operate in a coordinated manner under a unified timing reference.

[0108] In this embodiment, the data transmission module serves as the system's communication hub, undertaking the core function of data exchange. This module identifies, classifies, and processes received data according to a preset traffic priority strategy. Through traffic scheduling and shaping mechanisms, it ensures that critical data (such as control commands and real-time monitoring data) receives priority transmission, while simultaneously guaranteeing the transmission quality of different types of data.

[0109] In the system connectivity architecture, the remote test and control system establishes connections with the digital twin system, the device under test (DUT), and the physical test equipment via data transmission modules, enabling reliable issuance of control commands and real-time acquisition of status data. The digital twin system also maintains connections with the DUT and the physical test equipment via data transmission modules, ensuring bidirectional synchronization of test data between the virtual model and the physical devices.

[0110] This communication architecture design ensures real-time data interaction and time consistency among the various components of the system, providing a fundamental communication guarantee for the collaborative operation of the remote testing system. The time synchronization module ensures the consistency of the system's timing, while the data transmission module guarantees the reliability of critical data transmission through an intelligent scheduling mechanism. Together, they form an important foundation for the stable operation of the system.

[0111] In summary, all the above embodiments, such as Figure 6 As shown, a true-to-life test digital twin platform system is also provided to realize high-fidelity, deterministic, real-time, secure and interoperable remote testing of power distribution network equipment. The digital twin platform system includes a physical system, a digital twin system, a communication system and a remote test and control system.

[0112] 1. Physical systems, such as Figure 7As shown, it includes a local full-scale testing device, a remote device under test, and a sensing and edge processing module. The main contents of the sensing and edge processing module are as follows:

[0113] (1) Analyze the monitoring variables of key nodes of real test equipment and remote test equipment according to the test requirements, and deploy high-precision, high-frequency multimodal sensors, such as voltage sensors, current sensors, temperature sensors, partial discharge sensors, mechanical vibration sensors, etc.

[0114] (2) Deploy edge computing nodes to preprocess sensor data, implement denoising and feature extraction algorithms for specific signals, and configure PTP slave clock clients.

[0115] (3) The preprocessed data are all given a unified timestamp with microsecond precision through the PTP protocol and encapsulated into a standardized format to ensure that data from different sources and of different types are strictly aligned in time, providing a foundation for subsequent twin model calibration and state synchronization.

[0116] 2. Digital twin systems, such as Figure 8 As shown, it includes a high-fidelity digital twin model and a digital simulator. The digital simulator uses simulation software that supports transient, steady-state, and hardware-in-the-loop testing. The specific construction method of the high-fidelity digital twin model is as follows:

[0117] (1) Perform offline high-fidelity modeling for power equipment, establish a high-precision multi-physics model, and describe physical characteristics, electromagnetic characteristics, etc.

[0118] (2) Simulation analysis is performed based on a high-precision multiphysics model. Offline simulation is run to generate a large number of data snapshots. Model order reduction is then performed to transform the high-dimensional model into a low-dimensional state-space equation. Specifically, an intrinsic orthogonal decomposition method based on projection is used. The optimal orthogonal basis functions are extracted by performing singular value decomposition on the simulation snapshot matrix. Then, the original partial differential control equations are projected onto the low-dimensional subspace spanned by these intrinsic orthogonal decomposition equations through Galerkin projection, thereby obtaining a set of ordinary differential equations and realizing model order reduction. Similarity analysis is performed on the physical characteristics of the low-dimensional state-space equation and the high-dimensional model. If the similarity is lower than a preset threshold, it indicates that the order cannot be reduced, and the process proceeds to the third step. If the similarity is not lower than the preset threshold, it indicates that the order can be reduced, and the process proceeds to the fourth step.

[0119] (3) For models that cannot be downgraded, such as some ultrafast transient phenomena, a high-dimensional lookup table is formed based on a large number of data snapshots. A lightweight machine learning query model is trained using these data to support fast queries. The real-time performance of the overall simulation is guaranteed without sacrificing the accuracy of key details.

[0120] (4) Design a physical constraint graph neural network and couple its loss function with the control equations of the reduced-order model, the power grid topology and the real-time data interface. Use the data stream collected from the physical system in real time for training, dynamically correct the output of the reduced-order model, and realize accurate tracking of the physical entity state.

[0121] (5) Deploy the trained reduced-order model, surrogate model and physical constraint graph neural network onto the real-time simulator. During platform operation, use the real-time data stream from the physical system to continuously train and correct the physical constraint graph neural network online to ensure a high degree of consistency between the twin model and the physical entity.

[0122] 3. Communication systems, such as Figure 6 As shown, a TSN switch and a PTP master clock are deployed to provide the platform with a high-precision, deterministic clock, connecting the physical system and the digital twin system to ensure deterministic, low-latency, bidirectional data exchange and state synchronization between the two.

[0123] (1) The communication backbone of the entire test platform is constructed using TSN technology, and industrial Ethernet switches that support the TSN protocol are used.

[0124] (2) Network traffic is divided into multiple categories. The highest priority is used for PTP time synchronization and protection interlocking signals; the second highest priority is used for real-time sensor data and HIL closed-loop control signals; and the ordinary priority is used for non-critical data such as logs and monitoring. Each TSN switch has a network traffic control policy that defines which time slots each priority queue can start transmission and which time slots must be closed.

[0125] (3) Deploy the PTP master clock and configure the PTP slave clock on all network nodes, such as the sensing and edge module, digital emulator, real device interface, and device under test interface, to support the sub-microsecond network synchronization accuracy of the test system.

[0126] 4. Remote test and control system, including a test management module and integrated middleware. The test management module primarily uses manual editing of test cases or selection of adaptive test cases, followed by editing of test tasks and generation of test flows. Remote testing of the equipment is achieved through physical systems, digital twin systems, and communication systems. The adaptive test case generation process is as follows:

[0127] (1) The high-fidelity digital twin model is encapsulated into a simulator that conforms to the standard reinforcement learning environment interface. Based on the test objective, a quantified reward function is designed to guide the agent to explore scenarios that can test the limits of the device.

[0128] (2) Select a suitable reinforcement learning algorithm and conduct large-scale offline training until the agent's policy converges.

[0129] (3) Deploy the trained reinforcement learning agent to the test management module and interface with the digital twin model, digital simulator and physical system to realize trial and error training in the digital twin environment.

[0130] (4) Through millions of trial and error training, reinforcement learning agents can learn a strategy, namely, what action is most likely to trigger potential problems of the device under test in a given system state, and generate more targeted test cases.

[0131] The centralized middleware is a standardized middleware based on OPC UA, supporting remote device interoperability and solving the protocol heterogeneity problem of remote device access. The main process is as follows: ① Obtain the communication protocol manual of the device under test (DUT) to be connected, and analyze its data point table, data types, and control command formats. ② Use the OPC UA modeling tool to create a standardized information model XML file based on the analysis results, defining the device type, variables, and methods. For example, an inverter includes variable nodes such as DC voltage and output power, and method nodes such as start and stop. ③ Develop protocol adapter software to implement bidirectional data read / write logic between the proprietary protocol and the OPC UA information model. On one hand, it polls or subscribes to the proprietary protocol data of the DUT, and writes the data to the Value attribute of the corresponding node in the OPC UA server address space according to the predefined mapping relationship; on the other hand, it subscribes to changes in the Value of the control nodes in the OPC UA server, and translates these changes into proprietary protocol control commands that the DUT can understand and then sends them out. ④ Deploy the information model XML file and adapter plugin to the integration middleware, connect the DUT, and verify whether data can be successfully read and control commands can be sent out.

[0132] For example, based on the aforementioned digital twin platform system for full-scale testing, the remote overload capacity testing process for power electronic transformers is as follows:

[0133] (1) A 10kV / 400V power electronic transformer is connected to this test platform via network in a remote location.

[0134] (2) The integrated middleware of the remote test and control system communicates through its Modbus TCP interface, automatically parses its data point table, and creates a standardized digital profile for it in the OPC UA server, including variable nodes such as real-time electrical quantities, control parameters, and internal temperature measurement points.

[0135] (3) The remote test and control system performs a series of standard operating condition tests, such as no-load and rated load. Deployed sensors transmit the operating status data of the power electronic transformer to the digital twin system via the TSN network. The digital twin system performs online calibration of the electromagnetic-thermal coupling twin model of the power electronic transformer to ensure that the error between the IGBT junction temperature predicted by the twin model and the measurement value of the physical sensors is less than 2%.

[0136] (4) The test objective is to verify the emergency overload capacity of the power electronic transformer under high summer temperatures and high photovoltaic power generation conditions. The test engineer sets the constraints: ambient temperature 40℃, continuous overload of 150%, and assesses voltage stability and thermal stability. Based on these constraints, the reinforcement learning agent of the test management module autonomously explores in the digital twin and generates a test scenario sequence that includes high-frequency dynamic load fluctuations and background harmonics.

[0137] (5) The test scenario is first rehearsed in the digital twin for safety. After confirming that there is no risk, the remote test control system sends control commands to the remote power electronic transformer through OPC UA, and at the same time drives the power supply and load simulator in the laboratory to reproduce the scenario.

[0138] (6) Throughout the test, the platform monitored the various indicators of the power electronic transformer in real time and compared them with the prediction results of the digital twin. The results showed that after 20 minutes of overload, the heat sink temperature of the power electronic transformer reached 85℃, which was highly consistent with the prediction value (84.5℃) of the twin model, verifying its thermal performance under harsh working conditions.

[0139] The system described in this application can reduce testing costs and time, improve efficiency, and significantly reduce reliance on expensive physical experiments through extensive virtual simulations using digital twins. It achieves real-time synchronization across multiple time scales, enhancing collaboration. Based on a deterministic network and event-triggered mechanism using TSN, the synchronization error between physical testing and digital simulation is minimized, supporting collaborative verification of complex dynamic processes spanning from microseconds to seconds. It forms a test-twin closed loop, supporting autonomous optimization. Adaptive scenario generation and closed-loop verification technologies enable the digital twin model to self-correct using physical test data and generate more realistic test scenarios, creating a virtuous cycle of simulation guiding testing and testing optimizing simulation.

[0140] In some embodiments, such as Figure 9 As shown, a remote testing method for power grid equipment is also provided. This method is applied to the remote testing system for power grid equipment described above, and the method includes:

[0141] S101, the remote test control system acquires the test information of the device under test input by the user; the test information includes the test task, attribute information and the initial simulation model corresponding to the device under test.

[0142] S102, the digital twin system generates a target test scenario based on test information, test stimulus data of the real test equipment under standard operating conditions, and equipment response data of the device under test under the test stimulus data.

[0143] S103, the remote test control system performs tests on the device under test according to the target test scenario and obtains the test results.

[0144] The remote testing method for power grid equipment provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0145] 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.

[0146] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A remote testing system for power grid equipment, characterized in that, The remote testing system includes: at least one remote device under test (DUT), a digital twin system, a remote test control system, and a full-scale testing device; the remote test control system is connected to the digital twin system, the DUT, and the full-scale testing device, and the digital twin system is connected to the DUT and the full-scale testing device. The remote test control system is used to acquire test information of the device under test input by the user; the test information includes test tasks, attribute information, and the initial simulation model corresponding to the device under test; The digital twin system is used to generate a target test scenario based on the test information, the test stimulus data of the real-type test equipment under standard operating conditions, and the equipment response data of the device under test under the test stimulus data. The remote test control system is also used to test the device under test according to the target test scenario and obtain test results.

2. The remote testing system of claim 1, wherein, The remote test and control system includes: an integration middleware and a test management module; the integration middleware is connected to the test management module. The integrated middleware is used to obtain the test information of the device under test input by the user; The test management module is used to call the digital twin system to generate the target test scenario, and to test the device under test according to the target test scenario to obtain test results.

3. The remote testing system of claim 2, wherein, The integrated middleware includes: a model building unit and a protocol adaptation unit; the model building unit is connected to the protocol adaptation unit. The model building unit is used to create a standardized information model for the device under test based on the attribute information of the device under test when the device under test is connected. The protocol adaptation unit is used to enable the device under test to interact with other devices.

4. The remote testing system according to claim 2, characterized in that, The test management module includes: multiple instruction generation units and test units; The instruction generation unit is used to generate synchronized start instructions for the prototype test equipment, the device under test, and the digital twin system to start the prototype test equipment, the device under test, and the digital twin system. The testing unit is used to compare whether the actual measurement data output by the device under test and the simulation prediction data output by the digital twin system are consistent. If they are consistent, the test result is determined to be a pass; if they are inconsistent, the test result is determined to be a fail.

5. The remote testing system according to claim 4, characterized in that, The plurality of instruction generation units include: a first instruction generation unit, a second instruction generation unit, and a third instruction generation unit; The first instruction generation unit is used to generate a first instruction to control the full-scale test equipment to reproduce the power grid simulation signal corresponding to the target test scenario; The second instruction generation unit is used to generate a second instruction to control the device under test to operate under the power grid simulation signal and output the actual measurement data; The third instruction generation unit is used to generate a third instruction to control the digital twin system to perform tests based on the simulation signal and output the simulation prediction data; the simulation signal corresponds to the power grid simulation signal.

6. The remote testing system of any of claims 1-5, wherein, The digital twin system includes: a calibration module and a simulation exercise module; the calibration module is connected to the simulation exercise module. The correction module is used to correct the initial simulation model based on the test stimulus data and the device response data to obtain a high-fidelity digital twin model. The simulation exercise module is used to perform various test scenario exercises based on the test task to obtain the target test scenario.

7. The remote testing system according to claim 6, characterized in that, The correction module includes: a simulation unit and a correction unit; The simulation unit is used to input the test stimulus data into the initial simulation model for simulation testing and obtain simulation results; The correction unit is used to optimize the parameters of the initial simulation model based on the simulation results and the device response data to obtain the high-fidelity digital twin model.

8. The remote testing system according to claim 6, characterized in that, The digital twin system also includes: an optimization module; The optimization module is used to extract extreme scenarios from the multiple test scenarios and optimize the initial simulation model or the high-fidelity digital twin model based on the extreme scenarios.

9. The remote testing system according to claim 1, characterized in that, The remote testing system also includes a communication system, which includes a time synchronization module and a data transmission module. The time synchronization module is used to perform time synchronization processing on all access devices; The data transmission module is used to identify, schedule, and shape the received data according to a preset traffic priority strategy. The remote test and control system is connected to the digital twin system, the device under test (DUT), and the prototype test equipment via the data transmission module; the digital twin system is connected to the DUT and the prototype test equipment via the data transmission module; and the time synchronization module is connected to the clock nodes of the DUT, the digital twin system, the remote test and control system, and the prototype test equipment via the data transmission module.

10. A remote testing method for power grid equipment, characterized in that, The method is applied to a remote testing system for power grid equipment as described in any one of claims 1-9, and the method includes: The remote test control system acquires test information of the device under test input by the user; the test information includes test tasks, attribute information, and the initial simulation model corresponding to the device under test; The digital twin system generates a target test scenario based on the test information, the test stimulus data of the real-type test equipment under standard operating conditions, and the equipment response data of the device under test under the test stimulus data. The remote test control system performs tests on the device under test according to the target test scenario and obtains the test results.

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