Modularized secondary equipment flexible transformation method oriented to multi-scene adaptation

Abstract

The invention discloses a modular secondary equipment flexible transformation method for multi-scene adaptation, and belongs to the technical field of power system automation. The problems that in the prior art, the transformation cost is high, the period is long, and the influence on an existing system is large are solved, self-adaptive fine adjustment is conducted on the function logic model and the function parameters of the physical entity mirror image through the digital twin according to the real-time operation data, the adaptive capacity of the secondary equipment to different operation scenes is improved, and the practicability is high. False operation and refusal operation are effectively avoided; an optimal transformation or reconstruction scheme is automatically generated through the digital twinborn body and the scene adaptation knowledge base, and simulation verification is performed, so that the transformation cost is effectively reduced, and the transformation period is effectively shortened; the operation states of the equipment in different scenes are simulated by using the digital twinborn body, so that the time and the cost of field debugging and optimization are reduced, and the influence of transformation on the operation of the equipment is reduced.

Description

Technical Field

[0001] This invention relates to the field of power system automation technology, specifically to a modular secondary equipment flexible retrofitting method adaptable to multiple scenarios. Background Technology

[0002] In power systems, secondary equipment is an important component for ensuring the safe, stable, and economical operation of the power system. With the continuous development and upgrading of power systems, higher requirements are placed on the functions and performance of secondary equipment.

[0003] However, many traditional secondary devices in existing substations and power plants have problems such as high hardware integration, fixed functions, poor scalability, and inconsistent interfaces. When the operating scenario changes, such as the access of new energy sources, changes in load characteristics, adjustments to operating modes, or the need for functional upgrades, traditional secondary device modification methods have many problems, such as high modification costs, long cycles, and significant impact on existing systems.

[0004] Therefore, to address the shortcomings of existing methods, a modular, flexible retrofitting method for secondary equipment is proposed, adaptable to multiple scenarios. Summary of the Invention

[0005] The purpose of this invention is to provide a modular secondary equipment flexible retrofit method adaptable to multiple scenarios. By using a digital twin to adaptively fine-tune the functional parameters of the functional logic model and the physical entity mirror based on real-time operational data, it not only improves the adaptability of secondary equipment to different operating scenarios but also effectively avoids malfunctions and failures to operate. The optimal retrofit or reconstruction scheme is automatically generated through the digital twin and scenario adaptation knowledge base, and simulation verification is performed, effectively reducing retrofit costs and time. By using the digital twin to simulate the operating state of the equipment in different scenarios, the time and cost of on-site debugging and optimization are reduced, minimizing the impact of retrofits on equipment operation, thus solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A modular secondary equipment flexible retrofitting method adaptable to multiple scenarios includes the following steps:

[0008] The functional requirements of the target secondary equipment are decoupled and analyzed. Based on the results of the requirements analysis, the power system operation scenario is divided into several categories of functional modules, and each functional module corresponds to a set of functional requirements.

[0009] Based on the division of functional modules, a modular hardware system is constructed, and a standardized interface is designed for each functional module. The modular hardware system consists of standardized hardware functional modules and a flexible backplane.

[0010] Digital twins are created on the edge side for the modified physical secondary equipment. The digital twins include physical entity mirrors, functional logic models, and scenario adaptation knowledge bases.

[0011] Based on the target scenario information, the optimal transformation or reconstruction scheme is automatically generated through analysis using a digital twin and a scenario adaptation knowledge base, and then simulated and verified in the digital twin.

[0012] The verified functional configuration data and software packages are distributed to existing secondary devices for dynamic loading, enabling online reconstruction of device functions.

[0013] Furthermore, it also includes the following steps:

[0014] The target secondary equipment is evaluated to determine its hardware and software compatibility and the parts that can be modified.

[0015] Based on the evaluation results of existing secondary equipment and the functional requirements of the target scenario, a flexible transformation plan is designed.

[0016] The flexible transformation plan is optimized, and the newly defined functional modules are installed into the existing secondary equipment according to the flexible transformation plan, and connected to the hardware circuit of the existing secondary equipment through standardized interfaces.

[0017] The software packages for the new functional modules are integrated into the existing software system of the secondary equipment, and the modified secondary equipment is tested and verified.

[0018] Furthermore, digital twins are created for the modified physical secondary equipment based on the edge side, including:

[0019] By collecting real-time operating data of the modified physical secondary equipment through edge computing devices, an accurate image of its physical entity is created. The real-time operating data includes, but is not limited to, voltage, current, temperature and communication status.

[0020] Based on the flexible transformation plan, a functional logic model is constructed for each functional module, and the behavior and interaction of each functional module are simulated through the functional logic model.

[0021] Build scenario-adaptive knowledge bases for different operating scenarios to provide a basis for the dynamic adjustment of digital twins;

[0022] Real-time operating data of physical secondary equipment is synchronized to the physical entity mirror of the digital twin, and the functional logic model in the digital twin is dynamically updated according to changes in functional modules.

[0023] Furthermore, creating digital twins for the modified physical secondary equipment based on the edge also includes:

[0024] Real-time monitoring of the power system's operating status and identification of current operating scenarios;

[0025] Based on the information in the knowledge base adapted to the current operating scenario, automatically configure the functional module combination and optimization strategy of the digital twin;

[0026] Based on the functional requirements and operational data of the current operating scenario, the functional parameters of the functional logic model and the physical entity mirror are adaptively fine-tuned through the digital twin to adapt to different operating conditions.

[0027] By simulating different operating scenarios, the operating parameters and control strategies of physical secondary equipment are optimized.

[0028] Furthermore, the functional parameters of the functional logic model and the physical entity mirror image are adaptively fine-tuned through a digital twin, including:

[0029] The collected real-time running data is transmitted to the digital twin based on the MQTT communication protocol and stored in the scene adaptation knowledge base;

[0030] The real-time operating data is preprocessed and divided into event sequence data and steady-state operating data. Event sequence data includes protection action reports and fault recording files, while steady-state operating data includes PMU phasor measurement data, load curves, and equipment status variables.

[0031] Plot the load curve of the operating data, build a load forecasting model based on the load curve, and predict the load changes of physical secondary equipment in different time periods;

[0032] Analyze protection action records to establish protection action models and predict the triggering conditions for protection actions;

[0033] Based on the actual line impedance and load changes, the protection settings are dynamically adjusted through an optimization algorithm.

[0034] The optimized protection settings were simulated and verified to ensure that the protection action could be accurately triggered under different line impedance and load conditions, and to avoid false operation and failure to operate.

[0035] The functional parameters are fine-tuned based on the results of the optimization algorithm and updated in the functional logic model of the digital twin.

[0036] Furthermore, after updating the optimized functional parameters to the functional logic model of the digital twin, the following steps are taken:

[0037] The updated parameters are sent to the physical secondary devices via the network to achieve dynamic loading of functional parameters;

[0038] Continuously monitor the operating status of physical secondary equipment and feed new operating data back to the digital twin;

[0039] The digital twin continuously optimizes functional parameters based on new operational data, forming a closed-loop control.

[0040] Furthermore, based on target scenario information, analysis is performed using a digital twin and a scenario adaptation knowledge base to automatically generate the optimal transformation or reconstruction solution, including:

[0041] Collect relevant information about the target scenario, including but not limited to scenario type, device operating environment, functional requirements, and performance indicators;

[0042] Based on the target scenario information, relevant functional requirements, performance indicators and configuration rules are extracted from the scenario adaptation knowledge base;

[0043] By combining the physical entity mirror and functional logic model in the digital twin, the current state of the target device is analyzed;

[0044] Compare the functional requirements of the target scenario with the current state of the equipment to identify the parts that need to be modified or reconstructed;

[0045] Based on the configuration rules in the scenario adaptation knowledge base, generate transformation or reconstruction solutions.

[0046] Furthermore, simulation verification is performed in the digital twin, including:

[0047] The generated modification plan is simulated and verified in the digital twin to observe the operating performance of the equipment;

[0048] Based on the simulation results, the combination of functional modules and parameter settings in the transformation plan are optimized to ensure that the plan can meet the needs of the target scenario.

[0049] Analyze the simulation results to identify problems in the solution; adjust the combination of functional modules, parameter settings, and optimization strategies in the modification solution according to the problems; repeat the simulation verification until the solution meets the requirements of the target scenario;

[0050] The modification plan will be output in the form of a document or data package, including detailed information on the functional modules that need to be added or replaced, parameter settings, and optimization strategies.

[0051] Furthermore, the verified functional configuration data and software package will be dynamically loaded onto existing secondary devices, including:

[0052] Extract simulation-verified functional configuration data from the digital twin and package the functional configuration data into a standardized data package;

[0053] Extract the software package corresponding to the functional configuration data. The software package includes the executable code of the functional modules and related dependencies.

[0054] According to the communication protocol, the functional configuration data and software package are transmitted to the existing secondary equipment and securely verified through digital signatures;

[0055] The existing secondary equipment receives functional configuration data and software packages and performs verification; if verification fails, it sends an error report to the digital twin and requests retransmission of the data and software packages.

[0056] Furthermore, the process of dynamically loading verified functional configuration data and software packages into existing secondary devices also includes:

[0057] The verified functional configuration data and software package are loaded into memory, and the device's functional modules and parameter settings are dynamically updated based on the functional configuration data.

[0058] The updated functional modules are tested using existing secondary equipment, and the test results are fed back to the digital twin.

[0059] Based on the feedback, we continuously optimize the functional configuration data and software packages of the digital twin and monitor them in real time to form a closed-loop control.

[0060] Compared with the prior art, the beneficial effects of the present invention are:

[0061] 1. In this invention, the digital twin adaptively fine-tunes the functional parameters of the functional logic model and the physical entity mirror based on real-time operating data. This not only improves the adaptability of secondary equipment to different operating scenarios but also effectively avoids malfunctions and failures to operate. Furthermore, it feeds back new operating data to the digital twin, forming a closed-loop control system. This effectively predicts potential faults in functional modules, reduces equipment downtime and maintenance costs, and improves the availability and economy of secondary equipment.

[0062] 2. In this invention, the optimal transformation or reconstruction scheme is automatically generated by using a digital twin and a scenario adaptation knowledge base, and simulation verification is performed in the digital twin, which can effectively reduce the transformation cost and cycle; and by using the digital twin to simulate the operating status of the equipment in different scenarios, potential problems can be discovered in advance and the transformation scheme can be optimized, reducing the time and cost of on-site debugging and optimization, and further reducing the impact of transformation on equipment operation. Attached Figure Description

[0063] Figure 1 This is a flowchart of the modular secondary equipment flexible transformation method for adapting to multiple scenarios according to the present invention. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] To address the issues of high hardware integration, fixed functions, poor scalability, and inconsistent interfaces in existing traditional secondary equipment in substations and power plants; and to resolve the numerous problems that arise when traditional secondary equipment retrofitting methods change, such as high costs, long cycles, and significant impacts on existing systems, please refer to [the relevant documentation / reference]. Figure 1 This embodiment provides the following technical solution:

[0066] A modular secondary equipment flexible retrofitting method adaptable to multiple scenarios includes the following steps:

[0067] The functional requirements of the target secondary equipment are decoupled and analyzed, including but not limited to protection functions, monitoring functions, and communication functions. Based on the results of the requirements analysis, the power system operation scenario is divided into several categories of functional modules, and each functional module corresponds to a set of functional requirements.

[0068] Based on the functional module division, a modular hardware system is constructed, with standardized interfaces designed for each functional module. Each functional module corresponds to one hardware module, and the hardware modules are connected through standardized interfaces to ensure flexible combination and interchangeability between different functional modules. The modular hardware system consists of standardized hardware functional modules and a flexible backplane. The flexible backplane serves as the carrier and interconnection hub for all functional modules, providing a unified power distribution, data exchange, and clock synchronization channel. The type and number of slots on the flexible backplane can be pre-configured according to typical application scenarios, and a certain amount of redundant expansion slots are reserved.

[0069] The target secondary equipment is evaluated to determine its hardware and software compatibility and the parts that can be modified. Based on the evaluation results of the existing secondary equipment and the functional requirements of the target scenario, a flexible modification plan is designed. The modification plan includes the functional modules that need to be added or replaced, as well as the corresponding hardware and software modification measures. The flexible modification plan is optimized to ensure that the modified equipment meets the functional requirements of the target scenario while minimizing the changes and impacts on the existing system. According to the flexible modification plan, the new functional modules are installed in the existing secondary equipment and connected to the hardware circuit of the existing secondary equipment through standardized interfaces. The software packages of the new functional modules are integrated into the software system of the existing secondary equipment to ensure that the software modules can communicate and work together normally. The modified secondary equipment is tested and verified to ensure that it can work normally in the target scenario and meet the functional requirements.

[0070] A digital twin is created for the modified physical secondary equipment based on the edge side. The digital twin includes a physical entity image, a functional logic model, and a scenario adaptation knowledge base. The physical entity image accurately maps the module composition, connection relationships, CPU load, memory usage, channel occupancy, and real-time operating data of the physical equipment. The functional logic model, in the form of executable software, carries the business function logic currently running on the equipment. The scenario adaptation knowledge base stores the functional requirements, performance indicators, and configuration rules for secondary equipment in different application scenarios, such as: urban central stations, mountain wind power, and coastal photovoltaics; including:

[0071] Real-time operational data of the modified physical secondary equipment is collected through edge computing devices. For example, current transformers, voltage transformers, and temperature sensors are installed in key parts of the equipment to collect real-time operational data. A precise mirror image of the physical entity is created, with real-time operational data including but not limited to voltage, current, temperature, and communication status, ensuring that the physical entity mirror image reflects the actual operating status of the equipment in real time. A functional logic model is built for each functional module according to the flexible modification plan. The functional logic model includes the module's functional description, input / output interfaces, and internal logic flow, simulating the behavior and interaction of each functional module. A scenario adaptation knowledge base is built for different operating scenarios, including but not limited to information such as the equipment's functional requirements, module combinations, and optimization strategies in each scenario, providing a basis for the dynamic adjustment of the digital twin. The real-time operational data of the physical secondary equipment is synchronized to the physical entity mirror image of the digital twin, ensuring that the digital twin reflects the actual operating status of the equipment in real time. The functional logic model in the digital twin is dynamically updated according to changes in functional modules.

[0072] The system monitors the power system's operational status in real time and identifies current operational scenarios. Based on information in the knowledge base adapted to the current operational scenario, it automatically configures the functional module combinations and optimization strategies of the digital twin. According to the functional requirements and operational data of the current operational scenario, it adaptively fine-tunes the functional parameters of the functional logic model and the physical entity mirror image through the digital twin to adapt to different operating conditions. When functional modules of physical secondary equipment are replaced or upgraded, the corresponding functional logic model is updated synchronously to maintain consistency between the digital twin and the physical equipment. As operational scenarios change and equipment operational data accumulates, the system continuously updates the scenario adaptation knowledge base, optimizing the scenario adaptation strategies in the knowledge base through data analysis technology to improve the digital twin's adaptability to different scenarios. By simulating different operational scenarios, it optimizes the operating parameters and control strategies of physical secondary equipment to improve equipment operating efficiency and reliability.

[0073] Adaptive fine-tuning of functional parameters of the functional logic model and physical entity mirror image through digital twins includes:

[0074] The collected real-time operational data is transmitted to the digital twin based on the MQTT communication protocol and stored in the scenario adaptation knowledge base. The real-time operational data is preprocessed and divided into event sequence data and steady-state operational data. Event sequence data includes protection action reports and fault waveform files, recording the action time, action value, fault type, and current and voltage waveforms for several cycles before and after the fault. Steady-state operational data includes PMU phasor measurement data, load curves, and equipment status variables. The load curve of the operational data is plotted, and a load prediction model is built based on the load curve to predict the load changes of physical secondary equipment at different time periods. Protection action records are analyzed to establish a protection action model and predict the triggering conditions of protection actions. Based on the actual line impedance and load changes, the protection settings are dynamically adjusted through optimization algorithms. For example, the gradient descent method is used to optimize the protection settings by minimizing the protection action error. The optimized protection settings are simulated and verified to ensure that the protection action can be accurately triggered under different line impedance and load conditions, avoiding false actions and failures to act. The functional parameters are fine-tuned based on the results of the optimization algorithm and updated in the functional logic model of the digital twin. The updated parameters are distributed to the physical secondary equipment via the network to achieve dynamic loading of functional parameters; the operating status of the physical secondary equipment is continuously monitored, and the new operating data is fed back to the digital twin; the digital twin continuously optimizes the functional parameters based on the new operating data to form closed-loop control; for example, the protection settings are further optimized based on the new protection action records; the load prediction model is adjusted based on the new load curve.

[0075] The beneficial effects achieved by the above are as follows: the digital twin adaptively fine-tunes the functional parameters of the functional logic model and the physical entity mirror based on real-time operating data, which not only improves the adaptability of secondary equipment to different operating scenarios, but also effectively avoids malfunctions and failures to operate; and feeds back the new operating data to the digital twin to form closed-loop control, effectively predicts potential failures of functional modules, reduces equipment downtime and maintenance costs, and improves the availability and economy of secondary equipment.

[0076] Based on target scenario information, the system analyzes data using a digital twin and a scenario adaptation knowledge base to automatically generate the optimal modification or reconstruction solution, which is then simulated and verified within the digital twin. This includes:

[0077] Collect relevant information about the target scenario, including but not limited to scenario type, equipment operating environment, functional requirements, and performance indicators. For example, for urban central stations, collect information on high-precision protection requirements, real-time monitoring requirements, and equipment operating environment (such as temperature, humidity, and electromagnetic interference) through on-site surveys and technical document analysis. For mountain wind farms, collect information on strong communication requirements, fault diagnosis requirements, and equipment operating environment (such as wind speed, wind direction, and terrain). Based on the target scenario information, extract corresponding functional requirements, performance indicators, and configuration rules from the scenario adaptation knowledge base. For example, for urban central stations, extract high-precision protection requirements, real-time monitoring requirements, performance indicators such as response time and accuracy, and configuration rules such as module combinations and parameter settings. Combine the physical entity mirror and functional logic model in the digital twin to analyze the current state of the target equipment. For example, analyze whether the current equipment has high-precision protection and real-time monitoring functions, and whether the performance of these functions meets the requirements of the target scenario. Compare the functional requirements of the target scenario with the current state of the device to identify the parts that need to be modified or reconstructed; generate modification or reconstruction solutions based on the configuration rules in the scenario adaptation knowledge base; the solutions include functional modules that need to be added or replaced, parameter adjustments, optimization strategies, etc. For example, if the target scenario requires high-precision protection functions, but the current device's protection function accuracy is insufficient, then generate a solution to add a high-precision protection module and adjust the relevant parameters.

[0078] The generated modification scheme is simulated and verified in a digital twin to observe the equipment's operational performance, including the accuracy of protection actions, the real-time nature of monitoring data, and the stability of communication functions, ensuring the feasibility and effectiveness of the scheme. For example, the simulation can simulate a high-load operation scenario of a city central station to verify whether the equipment's protection functions can be triggered accurately and whether monitoring data can be transmitted in real time. Based on the simulation results, the functional module combination and parameter settings in the modification scheme are optimized to ensure that the scheme meets the needs of the target scenario. The simulation results are analyzed to identify problems in the scheme, such as insufficient performance and resource waste. Based on the problems, the functional module combination, parameter settings, and optimization strategies in the modification scheme are adjusted. For example, if the simulation results show that the protection action is delayed when the equipment is running under high load, the parameter settings of the protection module are adjusted to optimize its response speed. The simulation verification is repeated until the scheme meets the needs of the target scenario, ensuring the feasibility and effectiveness of the scheme. The modification scheme is output in the form of a document or data package, including detailed information on the functional modules that need to be added or replaced, parameter settings, and optimization strategies. For example, the output scheme document lists in detail the high-precision protection modules that need to be added, the adjusted protection settings, and the optimized communication parameters.

[0079] The verified functional configuration data and software package are distributed to existing secondary equipment for dynamic loading, enabling online reconfiguration of equipment functions; including:

[0080] The system extracts simulated and verified functional configuration data from the digital twin, packages this data into standardized data packages to ensure data integrity and consistency, extracts corresponding software packages containing executable code and dependencies for functional modules (e.g., extracting configuration data and software packages for high-precision protection modules to ensure correct loading into existing equipment), transmits the functional configuration data and software packages to existing secondary equipment according to a communication protocol, and performs security verification via digital signatures to ensure data security and integrity during transmission, receives and verifies the functional configuration data and software packages, and sends an error report to the digital twin if verification fails, requesting retransmission of the data and software packages, loads the verified functional configuration data and software packages into memory, and dynamically updates the device's functional modules and parameter settings based on the functional configuration data (e.g., loading the software package for the high-precision protection module on the device side, updating protection settings and related parameters), tests the updated functional modules on the existing secondary equipment to ensure normal operation, and feeds the test results back to the digital twin, continuously optimizing the digital twin's functional configuration data and software packages based on the feedback, and performing real-time monitoring to form a closed-loop control system.

[0081] The beneficial effects achieved by the above are as follows: by automatically generating the optimal transformation or reconstruction scheme through digital twins and scenario adaptation knowledge bases, and performing simulation verification in digital twins, the transformation cost and cycle can be effectively reduced; and by using digital twins to simulate the operating status of equipment in different scenarios, potential problems can be identified in advance and transformation schemes can be optimized, reducing the time and cost of on-site debugging and optimization, and further reducing the impact of transformation on equipment operation.

[0082] Working principle: By creating an accurate mirror image of the physical device, a functional logic model and a scenario adaptation knowledge base are constructed to provide a basis for dynamic adjustment of the device; based on the target scenario information, the optimal transformation plan is automatically generated through analysis of the digital twin and the knowledge base, and simulation verification is performed in the digital twin; the verified functional configuration data and software package are distributed to the existing device for dynamic loading to realize online functional reconstruction; and the device's operating status is continuously monitored, and new data is fed back to the digital twin to optimize functional parameters, forming a closed-loop control, thereby achieving efficient, reliable operation and adaptive optimization of the device in multiple scenarios.

[0083] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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.

[0084] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A modular secondary equipment flexible retrofitting method adaptable to multiple scenarios, characterized in that, Includes the following steps: The functional requirements of the target secondary equipment are decoupled and analyzed. Based on the results of the requirements analysis, the power system operation scenario is divided into several categories of functional modules, and each functional module corresponds to a set of functional requirements. Based on the division of functional modules, a modular hardware system is constructed, and a standardized interface is designed for each functional module. The modular hardware system consists of standardized hardware functional modules and a flexible backplane. Digital twins are created on the edge side for the modified physical secondary equipment. The digital twins include physical entity mirrors, functional logic models, and scenario adaptation knowledge bases. Based on the target scenario information, the optimal transformation or reconstruction scheme is automatically generated through analysis using a digital twin and a scenario adaptation knowledge base, and then simulated and verified in the digital twin. The verified functional configuration data and software packages are distributed to existing secondary devices for dynamic loading, enabling online reconstruction of device functions.

2. The modular secondary equipment flexible transformation method for multi-scenario adaptation as described in claim 1, characterized in that, It also includes the following steps: The target secondary equipment is evaluated to determine its hardware and software compatibility and the parts that can be modified. Based on the evaluation results of existing secondary equipment and the functional requirements of the target scenario, a flexible transformation plan is designed. The flexible transformation plan is optimized, and the newly defined functional modules are installed into the existing secondary equipment according to the flexible transformation plan, and connected to the hardware circuit of the existing secondary equipment through standardized interfaces. The software packages for the new functional modules are integrated into the existing software system of the secondary equipment, and the modified secondary equipment is tested and verified.

3. The modular secondary equipment flexible retrofitting method for multi-scenario adaptation as described in claim 2, characterized in that, Creating digital twins for modified physical secondary equipment based on the edge side includes: By collecting real-time operating data of the modified physical secondary equipment through edge computing devices, an accurate image of its physical entity is created. The real-time operating data includes, but is not limited to, voltage, current, temperature and communication status. Based on the flexible transformation plan, a functional logic model is constructed for each functional module, and the behavior and interaction of each functional module are simulated through the functional logic model. Build scenario-adaptive knowledge bases for different operating scenarios to provide a basis for the dynamic adjustment of digital twins; Real-time operating data of physical secondary equipment is synchronized to the physical entity mirror of the digital twin, and the functional logic model in the digital twin is dynamically updated according to changes in functional modules.

4. The modular secondary equipment flexible retrofitting method for multi-scenario adaptation as described in claim 3, characterized in that, Creating digital twins for modified physical secondary equipment based on the edge side also includes: Real-time monitoring of the power system's operating status and identification of current operating scenarios; Based on the information in the knowledge base adapted to the current operating scenario, automatically configure the functional module combination and optimization strategy of the digital twin; Based on the functional requirements and operational data of the current operating scenario, the functional parameters of the functional logic model and the physical entity mirror are adaptively fine-tuned through the digital twin to adapt to different operating conditions. By simulating different operating scenarios, the operating parameters and control strategies of physical secondary equipment are optimized.

5. The modular secondary equipment flexible retrofitting method for multi-scenario adaptation as described in claim 4, characterized in that, Adaptive fine-tuning of functional parameters of the functional logic model and physical entity mirror image through digital twins includes: The collected real-time running data is transmitted to the digital twin based on the MQTT communication protocol and stored in the scene adaptation knowledge base; The real-time operating data is preprocessed and divided into event sequence data and steady-state operating data. Event sequence data includes protection action reports and fault recording files, while steady-state operating data includes PMU phasor measurement data, load curves, and equipment status variables. Plot the load curve of the operating data, build a load forecasting model based on the load curve, and predict the load changes of physical secondary equipment in different time periods; Analyze protection action records to establish protection action models and predict the triggering conditions for protection actions; Based on the actual line impedance and load changes, the protection settings are dynamically adjusted through an optimization algorithm. The optimized protection settings were simulated and verified to ensure that the protection action was accurately triggered under different line impedance and load conditions. The functional parameters are fine-tuned based on the results of the optimization algorithm and updated in the functional logic model of the digital twin.

6. The modular secondary equipment flexible retrofitting method for multi-scenario adaptation as described in claim 5, characterized in that, After updating the optimized functional parameters to the functional logic model of the digital twin, the following steps are taken: The updated parameters are sent to the physical secondary devices via the network to achieve dynamic loading of functional parameters; Continuously monitor the operating status of physical secondary equipment and feed new operating data back to the digital twin; The digital twin continuously optimizes functional parameters based on new operational data, forming a closed-loop control.

7. The modular secondary equipment flexible retrofitting method for multi-scenario adaptation as described in claim 1, characterized in that, Based on target scenario information, analysis is performed using a digital twin and a scenario adaptation knowledge base to automatically generate the optimal transformation or reconstruction solution, including: Collect relevant information about the target scenario, including but not limited to scenario type, device operating environment, functional requirements, and performance indicators; Based on the target scenario information, relevant functional requirements, performance indicators and configuration rules are extracted from the scenario adaptation knowledge base; By combining the physical entity mirror and functional logic model in the digital twin, the current state of the target device is analyzed; Compare the functional requirements of the target scenario with the current state of the equipment to identify the parts that need to be modified or reconstructed; Based on the configuration rules in the scenario adaptation knowledge base, generate transformation or reconstruction solutions.

8. The modular secondary equipment flexible retrofitting method for multi-scenario adaptation as described in claim 7, characterized in that, Simulation verification within a digital twin includes: The generated modification plan is simulated and verified in the digital twin to observe the operating performance of the equipment; Based on the simulation results, the combination of functional modules and parameter settings in the transformation plan are optimized to ensure that the plan can meet the needs of the target scenario. Analyze the simulation results to identify problems in the solution; adjust the combination of functional modules, parameter settings, and optimization strategies in the modification solution according to the problems; repeat the simulation verification until the solution meets the requirements of the target scenario; The modification plan will be output in the form of a document or data package, including detailed information on the functional modules that need to be added or replaced, parameter settings, and optimization strategies.

9. The modular secondary equipment flexible retrofitting method for multi-scenario adaptation as described in claim 1, characterized in that, The verified functional configuration data and software package will be dynamically loaded into existing secondary devices, including: Extract simulation-verified functional configuration data from the digital twin and package the functional configuration data into a standardized data package; Extract the software package corresponding to the functional configuration data. The software package includes the executable code of the functional modules and related dependencies. According to the communication protocol, the functional configuration data and software package are transmitted to the existing secondary equipment and securely verified through digital signatures; The existing secondary equipment receives functional configuration data and software packages and performs verification; if verification fails, it sends an error report to the digital twin and requests retransmission of the data and software packages.

10. The modular secondary equipment flexible transformation method for multi-scenario adaptation as described in claim 9, characterized in that, The verified functional configuration data and software package will be dynamically loaded into existing secondary devices, and this also includes: The verified functional configuration data and software package are loaded into memory, and the device's functional modules and parameter settings are dynamically updated based on the functional configuration data. The updated functional modules are tested using existing secondary equipment, and the test results are fed back to the digital twin. Based on the feedback, we continuously optimize the functional configuration data and software packages of the digital twin and monitor them in real time to form a closed-loop control.

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