Maintenance decision-making platform for state detection and maintenance of direct current equipment

Abstract

The invention relates to the technical field of state maintenance of power equipment, in particular to a maintenance decision-making platform for state detection and maintenance of direct-current equipment. The system comprises an intelligent diagnosis and analysis module, a digital twinning online simulation module and a maintenance strategy recommendation module. The intelligent diagnosis and analysis module is used for collecting and processing multi-source heterogeneous data of the converter valve, extracting features, recognizing faults and carrying out real-time monitoring and early warning; the digital twinning online simulation module is used for constructing a high-precision digital twinning model of the converter valve and the operating environment of the converter valve, realizing data real-time synchronous interaction and simulation analysis, and outputting simulation verification data; and the maintenance strategy recommendation module is used for generating a maintenance decision scheme in combination with an optimization algorithm. The objective of the invention is to provide a maintenance decision-making platform for state detection and maintenance of direct current equipment so as to realize real-time monitoring, intelligent diagnosis, simulation prediction, decision optimization and real-time early warning management of converter valve equipment and improve maintenance and operation efficiency and equipment reliability.

Description

Technical Field

[0001] This invention relates to the field of condition-based maintenance technology for power equipment, and in particular to a maintenance decision platform for condition detection and maintenance of DC equipment. Background Technology

[0002] DC converter stations, as important hubs for power transmission, are key nodes for ensuring the safety of power transmission. With the expansion of power system scale and the increase in operational complexity, traditional DC equipment suffers from problems such as large size, lack of sharing and interoperability in maintenance operation management, and numerous data silos, resulting in enormous maintenance pressure on operation and maintenance personnel.

[0003] Currently, sensor technology, Internet of Things (IoT) technology, and big data analytics are widely used for monitoring converter valves both domestically and internationally. However, due to the complex structure of converter valves, existing technologies still have many shortcomings.

[0004] 1. Its operating environment is harsh, and data collection is difficult;

[0005] 2. Existing diagnostic technologies mostly rely on a single algorithm and have a single dimension of feature extraction, making it difficult to adapt to the complex characteristics of various types of faults in converter valves. Moreover, existing solutions generally lack high-precision simulation support and usually rely on real-time data for post-event diagnosis, making it impossible to predict potential risks through simulated faults.

[0006] 3. Predictive maintenance algorithms have low accuracy and are difficult to cope with uncertainties. At the same time, maintenance strategies are mostly generated based on fixed cycles or single health indicators, without being optimized in conjunction with equipment remaining life assessment and resource constraints. This can easily lead to over-maintenance, which increases costs, or under-maintenance, which causes failures.

[0007] Therefore, how to design a maintenance decision-making platform for DC equipment condition monitoring and maintenance is a technical problem that needs to be solved. Summary of the Invention

[0008] To address the aforementioned problems, the present invention aims to provide a maintenance decision-making platform for DC equipment condition detection and maintenance, enabling real-time monitoring, intelligent diagnosis, simulation prediction, optimized decision-making, and real-time early warning management of converter valve equipment, thereby improving maintenance efficiency and equipment reliability.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: including an intelligent diagnostic analysis module, a digital twin online simulation module, and a maintenance strategy recommendation module;

[0010] The intelligent diagnostic analysis module is used for the acquisition and processing of multi-source heterogeneous data of the converter valve, feature extraction and fault identification, and real-time monitoring and early warning.

[0011] The digital twin online simulation module is used to construct a high-precision digital twin model of the converter valve and its operating environment, receive the fault diagnosis results from the intelligent diagnostic analysis module, realize real-time data synchronization and simulation analysis, and output simulation verification data.

[0012] The maintenance strategy recommendation module is used to generate maintenance decision plans based on the equipment health status assessment results and in combination with optimization algorithms.

[0013] Furthermore, the intelligent diagnostic analysis module specifically includes a real-time monitoring and early warning submodule, a data preprocessing submodule, a feature extraction and recognition submodule, and a fault case submodule;

[0014] The real-time monitoring and early warning submodule is used to capture multi-source heterogeneous data of converter valve voltage, current, temperature and vibration in real time through multiple sensors, and realize fault early warning by combining real-time data with preset thresholds and model prediction, and output real-time monitoring data and early warning information.

[0015] The data preprocessing submodule is used to receive real-time monitoring data, remove data noise and outliers through data cleaning, and perform standardized transformation to output a high-quality data source.

[0016] The feature extraction and recognition submodule is used to extract key features from high-quality data sources and train a fault recognition model using a learning algorithm, to automatically classify and locate converter valve faults, and to perform online diagnosis through the fault recognition model, and output fault diagnosis results.

[0017] The fault case submodule is used to store fault phenomena, diagnostic results and handling solutions, build a historical fault case library, provide experience reference for fault diagnosis and maintenance strategy generation, and output historical case data.

[0018] Furthermore, the digital twin online simulation module specifically includes: a model building submodule, a data synchronization submodule, and a simulation verification submodule;

[0019] The model building submodule is used to build a three-dimensional model of the converter valve through three-dimensional modeling technology, and to analyze the electromagnetic field and thermal field distribution by combining physical field simulation analysis technology to build a high-precision digital twin model of the equipment and operating environment.

[0020] The data synchronization submodule is used to receive real-time monitoring data and fault diagnosis results from the intelligent diagnostic analysis module using a dedicated data interface and peer protocol, so as to realize real-time data interaction between the device and the digital twin model.

[0021] The simulation verification submodule is used to simulate the scope and development trend of the fault through a digital twin model; and output simulation verification data.

[0022] Furthermore, the maintenance strategy recommendation module specifically includes a health status assessment submodule and a monitoring strategy optimization submodule;

[0023] The health status assessment submodule is used to receive the diagnostic results from the intelligent diagnostic analysis module and the simulation data from the digital twin. It combines multi-stage stochastic optimization theory, Markov process, and health index theory to construct a health assessment model and output the equipment health level and remaining life assessment results.

[0024] The monitoring strategy optimization submodule is used to receive the health assessment results from the health status assessment submodule, and generate maintenance decision schemes by using optimization algorithms and combining them with constraints.

[0025] Furthermore, it also includes a user interaction module, which provides a visual display of device status, allows users to view maintenance suggestions, receive feedback on execution results, and query historical records.

[0026] The present invention has the following beneficial effects:

[0027] 1- This invention achieves virtual-real data synchronization by constructing a digital twin model, simulates typical fault conditions and outputs evolution data. Compared with existing digital twins that only focus on modeling and lack real-time linkage, this invention can provide early warning of risks and reduce the failure rate.

[0028] 2. This invention uses real-time monitoring and data analysis to promptly detect potential equipment faults, reduce unnecessary periodic maintenance, and improve the targeting and efficiency of maintenance work.

[0029] 3- This invention optimizes maintenance plans and resource allocation through intelligent decision support, reducing waste of manpower, material resources and financial resources, and lowering operation and maintenance costs. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0031] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0032] See Figure 1 As shown, the solution includes an intelligent diagnostic analysis module, a digital twin online simulation module, and a maintenance strategy recommendation module;

[0033] The intelligent diagnostic analysis module is used for the acquisition and processing of multi-source heterogeneous data of the converter valve, feature extraction and fault identification, and real-time monitoring and early warning.

[0034] The digital twin online simulation module is used to construct a high-precision digital twin model of the converter valve and its operating environment, receive the fault diagnosis results from the intelligent diagnostic analysis module, realize real-time data synchronization and simulation analysis, and output simulation verification data.

[0035] The maintenance strategy recommendation module is used to generate maintenance decision plans based on the equipment health status assessment results and in combination with optimization algorithms.

[0036] Furthermore, the intelligent diagnostic analysis module specifically includes a real-time monitoring and early warning submodule, a data preprocessing submodule, a feature extraction and recognition submodule, and a fault case submodule;

[0037] The real-time monitoring and early warning submodule is used to capture multi-source heterogeneous data of converter valve voltage, current, temperature and vibration in real time through multiple sensors, and realize fault early warning by combining real-time data with preset thresholds and model prediction, and output real-time monitoring data and early warning information.

[0038] The data preprocessing submodule is used to receive real-time monitoring data, remove data noise and outliers through data cleaning, and perform standardized transformation to output a high-quality data source.

[0039] The feature extraction and recognition submodule is used to extract key features from high-quality data sources and train a fault recognition model using learning algorithms of convolutional neural networks and support vector machines. It automatically classifies and locates converter valve faults, performs online diagnosis through the fault recognition model, and outputs fault diagnosis results.

[0040] The fault case submodule is used to store fault phenomena, diagnostic results and handling solutions, build a historical fault case library, provide experience reference for fault diagnosis and maintenance strategy generation, and output historical case data.

[0041] Furthermore, the digital twin online simulation module specifically includes: a model building submodule, a data synchronization submodule, and a simulation verification submodule;

[0042] The model building submodule is used to build a three-dimensional model of the converter valve through three-dimensional modeling technology, and to analyze the electromagnetic field and thermal field distribution by combining physical field simulation analysis technology to build a high-precision digital twin model of the equipment and operating environment.

[0043] The data synchronization submodule is used to receive real-time monitoring data and fault diagnosis results from the intelligent diagnostic analysis module using a dedicated data interface and peer protocol, so as to realize real-time data interaction between the device and the digital twin model.

[0044] The simulation verification submodule is used to simulate the scope and development trend of the fault through a digital twin model; and output simulation verification data.

[0045] Furthermore, the maintenance strategy recommendation module specifically includes a health status assessment submodule and a monitoring strategy optimization submodule;

[0046] The health status assessment submodule is used to receive the diagnostic results from the intelligent diagnostic analysis module and the simulation data from the digital twin. It combines multi-stage stochastic optimization theory, Markov process, and health index theory to construct a health assessment model and output the equipment health level and remaining life assessment results.

[0047] The monitoring strategy optimization submodule is used to receive the health assessment results from the health status assessment submodule, and generate maintenance decision schemes by using optimization algorithms and combining them with constraints.

[0048] Furthermore, it also includes a user interaction module, which provides a visual display of device status, allows users to view maintenance suggestions, receive feedback on execution results, and query historical records.

[0049] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0050] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0051] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0052] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A maintenance decision-making platform for condition monitoring and maintenance of DC equipment, characterized in that, It includes an intelligent diagnostic analysis module, a digital twin online simulation module, and a maintenance strategy recommendation module; The intelligent diagnostic analysis module is used for the acquisition and processing of multi-source heterogeneous data of the converter valve, feature extraction and fault identification, and real-time monitoring and early warning. The digital twin online simulation module is used to construct a high-precision digital twin model of the converter valve and its operating environment, receive the fault diagnosis results from the intelligent diagnostic analysis module, realize real-time data synchronization and simulation analysis, and output simulation verification data. The maintenance strategy recommendation module is used to generate maintenance decision plans based on the equipment health status assessment results and in combination with optimization algorithms.

2. The maintenance decision-making platform for DC equipment condition detection and maintenance according to claim 1, characterized in that, The intelligent diagnostic analysis module specifically includes a real-time monitoring and early warning submodule, a data preprocessing submodule, a feature extraction and recognition submodule, and a fault case submodule. The real-time monitoring and early warning submodule is used to capture multi-source heterogeneous data of converter valve voltage, current, temperature and vibration in real time through multiple sensors, and realize fault early warning by combining real-time data with preset thresholds and model prediction, and output real-time monitoring data and early warning information. The data preprocessing submodule is used to receive real-time monitoring data, remove data noise and outliers through data cleaning, and perform standardized transformation to output a high-quality data source. The feature extraction and recognition submodule is used to extract key features from high-quality data sources and train a fault recognition model using a learning algorithm, to automatically classify and locate converter valve faults, and to perform online diagnosis through the fault recognition model, and output fault diagnosis results. The fault case submodule is used to store fault phenomena, diagnostic results and handling solutions, build a historical fault case library, provide experience reference for fault diagnosis and maintenance strategy generation, and output historical case data.

3. The maintenance decision-making platform for DC equipment condition detection and maintenance according to claim 1, characterized in that, The digital twin online simulation module specifically includes: a model building submodule, a data synchronization submodule, and a simulation verification submodule; The model building submodule is used to build a three-dimensional model of the converter valve through three-dimensional modeling technology, and to analyze the electromagnetic field and thermal field distribution by combining physical field simulation analysis technology to build a high-precision digital twin model of the equipment and operating environment. The data synchronization submodule is used to receive real-time monitoring data and fault diagnosis results from the intelligent diagnostic analysis module using a dedicated data interface and peer protocol, so as to realize real-time data interaction between the device and the digital twin model. The simulation verification submodule is used to simulate the scope and development trend of the fault through a digital twin model; and output simulation verification data.

4. The maintenance decision-making platform for DC equipment condition detection and maintenance according to claim 1, characterized in that, The maintenance strategy recommendation module specifically includes a health status assessment submodule and a monitoring strategy optimization submodule. The health status assessment submodule is used to receive the diagnostic results from the intelligent diagnostic analysis module and the simulation data from the digital twin. It combines multi-stage stochastic optimization theory, Markov process, and health index theory to construct a health assessment model and output the equipment health level and remaining life assessment results. The monitoring strategy optimization submodule is used to receive the health assessment results from the health status assessment submodule, and generate maintenance decision schemes by using optimization algorithms and combining them with constraints.

5. A maintenance decision-making platform for DC equipment condition monitoring and maintenance according to claim 1, characterized in that, It also includes a user interaction module, which provides a visual display of equipment status, allows users to view maintenance suggestions, receive feedback on execution results, and query historical records.

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