Intelligent substation operation and maintenance system based on meta universe technology and working method of intelligent substation operation and maintenance system

Abstract

The invention discloses an intelligent substation operation and maintenance system based on a meta universe technology and a working method thereof, and belongs to the technical field of intelligent operation and maintenance of power systems. Traditional substation operation and maintenance depend on manpower, efficiency is low, real-time performance is poor, and faults are difficult to predict. The system comprises a data acquisition layer used for acquiring equipment operation, environment and video data in real time; the data transmission layer is used for encrypting transmission data; the data processing layer comprises a module for preprocessing, analyzing and diagnosing data and constructing a dynamic three-dimensional model; the meta cosmic interaction layer provides virtual reality, augmented reality and virtual digital human interaction functions; and the decision support layer generates an operation and maintenance strategy and an emergency plan according to an analysis result. By constructing a virtual-real fusion operation and maintenance environment, real-time monitoring, intelligent diagnosis and efficient operation and maintenance of the substation equipment are realized, and the operation and maintenance efficiency and the system reliability are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent operation and maintenance technology for power systems, and in particular to an intelligent substation operation and maintenance system and its working method based on metaverse technology. Background Technology

[0002] With the continuous growth of electricity demand and the ongoing expansion of the power grid, the safe and stable operation of substations, as key hubs in the power system, is of paramount importance. Traditional substation operation and maintenance methods mainly rely on manual inspection and operation, which suffers from low efficiency, poor real-time performance, and inability to detect potential faults in a timely manner. Maintenance personnel typically judge the level of equipment defects based on visual inspection and experience, which is prone to false positives and false negatives, and lacks effective technical support when facing complex faults.

[0003] In recent years, with the development of digital technology, the power industry has begun to explore the application of emerging technologies to substation operation and maintenance. For example, since 2013, power grid companies have been tendering for substation inspection robots, attempting to use robots to assist manual inspections. However, existing inspection robots are limited by space and environmental factors, making it difficult to achieve all-terrain inspections, and they suffer from problems such as insufficient real-time equipment inspection and low validity of inspection results. In addition, existing systems often lack holistic intelligent decision support, making it difficult to achieve effective virtual-physical integration and intelligent operation and maintenance management.

[0004] Metaverse technology, as a culmination of technologies such as digital twins, 5G, cloud computing, XR, artificial intelligence, and blockchain, offers the possibility of constructing a "power metaverse" that fully maps and interacts with the physical power grid in virtual space in real time. However, how to effectively apply metaverse technology to substation operation and maintenance to achieve true virtual-real integration and intelligent decision-making remains a technical challenge that needs to be addressed. Summary of the Invention

[0005] The technical problem to be solved and the technical task proposed by this invention is to improve and refine existing technical solutions, and to provide an intelligent substation operation and maintenance system and its working method based on metaverse technology, with the aim of achieving intelligent and efficient substation operation and maintenance. To this end, this invention adopts the following technical solution.

[0006] A smart substation operation and maintenance system based on metaverse technology includes: The data acquisition layer is used to collect real-time operating data, environmental data, and video data of the primary electrical equipment in the substation. The data acquisition layer includes temperature sensors, vibration sensors, partial discharge sensors, current sensors, and voltage sensors deployed on transformers, circuit breakers, disconnect switches, and instrument transformers to monitor the operating status of the equipment; it also includes an environmental monitoring module that monitors environmental parameters within the substation through temperature and humidity sensors, wind speed sensors, and rainfall sensors; and a video acquisition module that collects on-site video streams through high-definition cameras located at key locations in the substation. The data transmission layer uses a 5G communication network or optical fiber to transmit encrypted data acquired by the data acquisition layer to the data processing layer in real time. The data processing layer includes a data preprocessing module, a data analysis and intelligent diagnosis module, and a 3D modeling module. The data preprocessing module cleans, denoises, and normalizes the collected data. The data analysis and intelligent diagnosis module uses artificial intelligence algorithms to analyze the preprocessed data, constructs equipment fault prediction models, and realizes intelligent fault risk diagnosis and early warning. The 3D modeling module constructs a high-precision 3D model of the substation based on laser scanning data and UAV aerial photography data, and dynamically renders the model according to real-time data, using color changes or flashing effects to indicate abnormal equipment status. The metaverse interaction layer includes a virtual reality module, an augmented reality module, and a virtual digital human module. The virtual reality module provides an immersive virtual substation scene through a VR headset, allowing maintenance personnel to perform equipment inspections, view parameters, and simulate operations. The augmented reality module overlays equipment operating parameters and fault warning information onto the real scene through AR glasses to assist on-site operations. The virtual digital human module has voice recognition and natural language processing functions, providing operation guidance and training support for maintenance personnel. The decision support layer includes an operation and maintenance strategy formulation module and an emergency response module. The operation and maintenance strategy formulation module generates the optimal operation and maintenance strategy based on data analysis results, combined with equipment importance and maintenance plans, through optimization algorithms. The emergency response module matches emergency plans according to fault types and guides operation and maintenance personnel to handle faults through the metaverse interaction layer.

[0007] By constructing a five-layer system architecture that includes data acquisition, transmission, processing, metaverse interaction, and decision support, the deep integration of the physical space of the substation and the virtual space of the metaverse is realized, effectively solving the problems of low efficiency and poor real-time performance of traditional operation and maintenance methods, and realizing the intelligent and efficient operation and maintenance of substations.

[0008] As a preferred technical approach, the data analysis and intelligent diagnosis module employs Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), or Long Short-Term Memory (LSTM) algorithms to perform trend analysis on equipment operating data, enabling intelligent prediction and diagnosis of potential equipment faults. By adopting advanced artificial intelligence algorithms, the accuracy of fault prediction and the level of intelligent diagnosis are significantly improved.

[0009] As a preferred technical approach, the 3D modeling module constructs a virtual model consistent with the physical substation using digital twin technology, integrating equipment operation information, spatial information, and environmental information to achieve virtual-real mapping and dynamic interaction. Utilizing digital twin technology to achieve high-precision virtual-real mapping provides more intuitive and reliable data support for operation and maintenance decisions.

[0010] As a preferred technical approach, the metaverse interaction layer supports remote collaboration, allowing on-site maintenance personnel to share first-person perspective views via AR glasses, while remote experts provide real-time guidance through annotation and document push notifications. This enables remote collaborative work and effectively addresses the issues of insufficient skills among on-site maintenance personnel and a scarcity of expert resources.

[0011] As a preferred technical approach, the system also integrates a collaborative operation module for drones and inspection robots. The drones, equipped with high-definition cameras and infrared sensors, perform equipment appearance inspections and defect identification. The inspection robots possess all-terrain adaptive capabilities, performing instrument reading recognition, abnormal heat detection, and auxiliary maintenance operations. Through the collaborative operation of drones and robots, the inspection range is expanded, and inspection efficiency and safety are improved.

[0012] A working method for an intelligent substation operation and maintenance system based on metaverse technology includes the following steps: 1) Real-time data acquisition of substation primary electrical equipment operation data, environmental data, and video data is collected through the data acquisition layer and then encrypted and transmitted to the data processing layer through the data transmission layer; 2) The data processing layer preprocesses the collected data, uses artificial intelligence algorithms for fault diagnosis and prediction, and updates the status information of the 3D model at the same time; 3) Maintenance personnel enter virtual scenes or augmented reality interfaces through the metaverse interaction layer to conduct equipment inspections, operation simulations, and troubleshooting, with virtual digital humans providing real-time guidance; 4) The decision support layer generates operation and maintenance strategies or emergency plans based on the diagnostic results. After the operation and maintenance personnel execute them, they feed back the results to the system, forming a closed-loop management.

[0013] Through a systematic workflow, closed-loop management from data collection to decision execution has been achieved, significantly improving operational efficiency and quality.

[0014] As a preferred technical approach: In the metaverse interaction phase, maintenance personnel use VR headsets to inspect equipment along preset paths within a virtual environment. When the model indicates an equipment malfunction, they can retrieve real-time parameters, historical data, and handling suggestions; or they can use AR glasses to view overlaid virtual information on the real device and complete the maintenance operations according to the instructions. This provides an immersive interactive experience, with various data displayed in real-time, improving the convenience and effectiveness of maintenance.

[0015] As a preferred technical approach: when the system detects a device malfunction, it automatically triggers an emergency response process: the data processing layer locates the cause of the malfunction, the decision support layer matches a contingency plan, and the metaverse interaction layer demonstrates the operation process through a virtual digital human and guides on-site personnel to perform maintenance via AR glasses. This achieves automation and intelligence in emergency response, significantly shortening fault handling time. The AR mode enhances the sense of presence and technical support, enabling better fault detection and location, and improving the convenience of power grid maintenance.

[0016] Beneficial Effects: By constructing an intelligent substation operation and maintenance system based on metaverse technology, a deep integration of the physical and virtual spaces of the substation is achieved, significantly improving operation and maintenance efficiency and quality. Through multi-source data acquisition and intelligent analysis, the system enables real-time monitoring of equipment status and accurate fault prediction; the metaverse interaction layer provides an immersive experience and remote collaboration functions, effectively enhancing the convenience and security of operation and maintenance tasks; and the intelligent strategy generation of the decision support layer enables optimized allocation of operation and maintenance resources and rapid fault handling. The overall system boasts advantages such as high intelligence, strong real-time performance, and significantly improved operation and maintenance efficiency, providing a strong guarantee for the safe and stable operation of substations. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system architecture of the present invention.

[0018] Figure 2 This is a schematic diagram of the workflow of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings.

[0020] The intelligent substation operation and maintenance system of this invention achieves deep integration and real-time interaction between the physical substation and the virtual space of the metaverse by constructing a digital twin. The overall system architecture is shown in the attached figure. Figure 1 The diagram illustrates the five-layer structure of the system: data acquisition layer, data transmission layer, data processing layer, metaverse interaction layer, and decision support layer, along with their logical relationships and data flow. Each layer exchanges data through standardized interface protocols, ensuring system compatibility and scalability.

[0021] The system's hardware infrastructure includes various sensors, cameras, inspection robots, and drones deployed at the substation site. The data processing layer can be deployed on an on-site edge server or a cloud server, requiring a high-performance GPU to support 3D rendering and AI algorithm calculations. The metaverse interaction layer relies on VR headsets such as HTC Vive Pro2 or AR glasses such as Microsoft HoloLens 2, as well as graphics workstations supporting large-scale real-time rendering. This hardware is connected via a high-speed network, collectively supporting the system's stable operation.

[0022] Example 1: Daily Inspection of Substations Based on Digital Twin This embodiment details the application of the system in the daily inspection of substations.

[0023] As attached Figure 2 As shown, the system workflow begins at the data acquisition layer.

[0024] S1: At the physical substation site, temperature sensors, vibration sensors, partial discharge sensors, current sensors, and voltage sensors are deployed on key primary electrical equipment such as transformers, circuit breakers, disconnect switches, and instrument transformers. These sensors collect the equipment's operating parameters in real time at a set sampling frequency. The environmental monitoring module continuously monitors environmental parameters through temperature and humidity sensors, wind speed sensors, and rainfall sensors deployed within the substation. The video acquisition module acquires real-time video streams from the site using high-definition cameras installed at key locations within the substation. All collected data is encrypted via a 5G communication network or fiber optic network used in the data transmission layer before being transmitted in real time to edge computing nodes within the substation or a remote data processing center.

[0025] S2: The data transmission layer ensures that data arrives stably and securely at the data processing layer. The data preprocessing module in the data processing layer first cleans, denoises, and normalizes the received raw data to eliminate outliers and noise interference. Subsequently, the data analysis and intelligent diagnosis module uses pre-set artificial intelligence algorithms, such as a fault prediction model built using the Long Short-Term Memory (LSTM) algorithm, to analyze the processed data. This module can identify abnormal trends in equipment operating parameters and generate preliminary diagnostic results and early warning information. For example, by analyzing data such as transformer oil temperature, winding temperature, and load current using a trained LSTM model, it can predict whether the transformer is at risk of overheating. Simultaneously, the 3D modeling module uses laser scanning point cloud data, orthophotos from UAV aerial photography, and oblique photogrammetry data to construct a high-precision 3D model corresponding to the physical substation at a 1:1 scale using 3D modeling software. This model is not statically displayed but is updated based on real-time equipment data through a built-in dynamic rendering engine. For example, when the winding temperature of a transformer exceeds a preset threshold, the transformer 201 in the 3D model will dynamically change from green, representing normal operation, to a warning red with a flashing effect, thus visually mapping the abnormal state of the physical entity in the virtual space.

[0026] S3: Maintenance personnel wearing VR headsets enter an immersive virtual substation scene constructed by the metaverse interaction layer. In this scene, maintenance personnel control their virtual avatar from a first-person perspective, moving along a preset or customized inspection path. When the virtual avatar approaches equipment marked as abnormal in the 3D model, such as a transformer, the maintenance personnel can interact with the equipment using a controller to retrieve and display an information panel. The panel centrally displays the equipment's real-time operating parameters, historical data curves, maintenance records, etc. For example, when approaching a transformer, the controller allows viewing the transformer's real-time operating parameters, such as oil temperature, winding temperature, and load current, as well as its historical operating data and maintenance records. If the model shows an abnormally high transformer temperature, a flashing red indicator will alert the maintenance personnel, allowing them to further analyze the cause of the anomaly and suggest appropriate handling measures.

[0027] S4: If maintenance personnel need to further confirm or perform operations, the data analysis and intelligent diagnosis module determines that the transformer may have a heat dissipation problem based on data changes. The maintenance strategy formulation module of the decision support layer will comprehensively consider the importance of the equipment, historical maintenance data, and current diagnostic results, and formulate specific maintenance strategies through optimization algorithms, such as "check the transformer cooling fan and clean the heat sink." After confirming the operation steps in the virtual environment, maintenance personnel can perform the maintenance on-site and feed back the final processing results to the system through the system terminal, forming a closed-loop management.

[0028] All data from this inspection process, including interactive operations, diagnostic results, and execution feedback, were recorded by the system for archiving and optimization of the algorithm model.

[0029] Example 2: Emergency Response to Substation Faults This embodiment details the application of the system in emergency response to sudden faults in substations.

[0030] When a circuit breaker in a 110kV substation suddenly trips, various sensors and cameras in the data acquisition layer immediately activate, collecting equipment operation data, environmental data, and on-site video data at the moment of the fault. This data is then rapidly transmitted to the data processing layer via a high-speed communication network.

[0031] The data preprocessing module in the data processing layer quickly filters and cleans massive amounts of fault data. The data analysis and intelligent diagnosis module then initiates multiple artificial intelligence algorithms for parallel analysis. By comparing the transient changes in equipment operating parameters before and after the fault and calling the historical fault case library for pattern matching, it quickly diagnoses the cause of the fault as "mechanical failure of the circuit breaker operating mechanism". The 3D modeling module synchronously updates the virtual substation scene, and the model of the faulty circuit breaker displays an abnormal state indicator.

[0032] The emergency response module in the decision support layer automatically matches the optimal response plan from the emergency plan library based on the diagnosed fault type. Once the plan is determined, guidance is provided to on-site maintenance personnel through the metaverse interaction layer. On-site maintenance personnel, wearing AR glasses, arrive at the fault site. Through the AR glasses, they can see both the real equipment scene and virtual information superimposed on the real equipment, including key parameter annotations, operating procedure instructions, and a virtual expert avatar generated by the virtual digital human module. This virtual digital human has voice interaction capabilities and can demonstrate the correct operating procedures step-by-step through animation, such as "First, check if the operating mechanism linkage has come loose...". Maintenance personnel can perform maintenance operations on the real circuit breaker according to the guidance displayed in the AR glasses and the demonstration by the virtual digital human. During the maintenance process, maintenance personnel can also communicate with remote experts via voice through the microphone built into the AR glasses or request further text and image support through gesture recognition. After the fault is resolved, the maintenance personnel feed the processing results back to the system through the AR glasses interface. The system will instruct the data acquisition layer to perform test data acquisition on the repaired equipment. After confirming that the fault has been eliminated, the equipment status will be updated to normal in the 3D model, and the event log of this fault will be fully recorded, including the time, diagnosis cause, handling steps and results, to accumulate data for equipment status assessment and life prediction.

[0033] The above-described specific embodiments of the present invention demonstrate the outstanding substantive features and significant progress of the present invention. Based on actual usage needs, equivalent modifications in shape, structure, etc., can be made to the present invention, and all such modifications are within the scope of protection of this solution.

Claims

1. A smart substation operation and maintenance system based on metaverse technology, characterized in that: include: The data acquisition layer is used to collect real-time operating data, environmental data, and video data of the primary electrical equipment in the substation. The data acquisition layer includes temperature sensors, vibration sensors, partial discharge sensors, current sensors, and voltage sensors deployed on transformers, circuit breakers, disconnect switches, and instrument transformers to monitor the operating status of the equipment. It also includes an environmental monitoring module that monitors environmental parameters in the substation through temperature and humidity sensors, wind speed sensors, and rainfall sensors. And a video acquisition module, which uses high-definition cameras deployed at key locations in the substation to capture on-site video streams; The data transmission layer uses a 5G communication network or optical fiber to transmit encrypted data acquired by the data acquisition layer to the data processing layer in real time. The data processing layer includes a data preprocessing module, a data analysis and intelligent diagnosis module, and a 3D modeling module; The data preprocessing module cleans, denoises, and normalizes the collected data; the data analysis and intelligent diagnosis module uses artificial intelligence algorithms to analyze the preprocessed data, builds equipment fault prediction models, and realizes intelligent diagnosis and early warning of fault risks; the 3D modeling module builds a high-precision 3D model of the substation based on laser scanning data and UAV aerial photography data, and dynamically renders the model according to real-time data, indicating abnormal equipment status through color changes or flashing effects. The metaverse interaction layer includes a virtual reality module, an augmented reality module, and a virtual digital human module. The virtual reality module provides an immersive virtual substation scene through a VR headset, allowing maintenance personnel to perform equipment inspections, view parameters, and simulate operations. The augmented reality module overlays equipment operating parameters and fault warning information onto the real scene through AR glasses to assist on-site operations. The virtual digital human module has voice recognition and natural language processing functions, providing operation guidance and training support for maintenance personnel. The decision support layer includes an operation and maintenance strategy formulation module and an emergency response module. The operation and maintenance strategy formulation module generates the optimal operation and maintenance strategy based on data analysis results, combined with equipment importance and maintenance plans, through optimization algorithms. The emergency response module matches emergency plans according to fault types and guides operation and maintenance personnel to handle faults through the metaverse interaction layer.

2. The intelligent substation operation and maintenance system based on metaverse technology according to claim 1, characterized in that: The data analysis and intelligent diagnosis module uses convolutional neural network (CNN), recurrent neural network (RNN), or long short-term memory (LSTM) algorithms to perform trend analysis on equipment operation data, thereby enabling intelligent prediction and diagnosis of potential equipment faults.

3. The intelligent substation operation and maintenance system based on metaverse technology according to claim 1, characterized in that: The 3D modeling module uses digital twin technology to construct a virtual model consistent with the physical substation, and integrates equipment operation information, spatial information and environmental information to achieve virtual-real mapping and dynamic interaction.

4. The intelligent substation operation and maintenance system based on metaverse technology according to claim 1, characterized in that: The metaverse interaction layer supports remote collaboration, allowing on-site maintenance personnel to share first-person perspective views through AR glasses, and remote experts to provide real-time guidance through annotation and document push.

5. The intelligent substation operation and maintenance system based on metaverse technology according to claim 1, characterized in that: The system also integrates a collaborative operation module for drones and inspection robots; the drones are equipped with high-definition cameras and infrared sensors to perform equipment appearance inspections and defect identification; the inspection robots have all-terrain adaptive capabilities and can perform instrument reading recognition, abnormal heat detection, and auxiliary maintenance operations.

6. A working method for an intelligent substation operation and maintenance system based on metaverse technology as described in any one of claims 1-5, characterized in that... Includes the following steps: 1) Real-time data acquisition of substation primary electrical equipment operation data, environmental data, and video data is collected through the data acquisition layer and then encrypted and transmitted to the data processing layer through the data transmission layer; 2) The data processing layer preprocesses the collected data, uses artificial intelligence algorithms for fault diagnosis and prediction, and updates the status information of the 3D model at the same time; 3) Maintenance personnel enter virtual scenes or augmented reality interfaces through the metaverse interaction layer to conduct equipment inspections, operation simulations, and troubleshooting, with virtual digital humans providing real-time guidance; 4) The decision support layer generates operation and maintenance strategies or emergency plans based on the diagnostic results. After the operation and maintenance personnel execute them, they feed back the results to the system, forming a closed-loop management.

7. The working method of an intelligent substation operation and maintenance system based on metaverse technology according to claim 6, characterized in that: In the metaverse interaction phase, maintenance personnel use VR headsets to inspect virtual scenes along preset paths. When the model indicates an equipment malfunction, they can retrieve real-time parameters, historical data, and handling suggestions; or they can use AR glasses to view the superimposed virtual information on real devices and complete the maintenance operations according to the instructions.

8. The working method of an intelligent substation operation and maintenance system based on metaverse technology according to claim 6, characterized in that: When the system detects a device malfunction, it automatically triggers an emergency response process: the data processing layer locates the cause of the malfunction, the decision support layer matches the contingency plan, and the metaverse interaction layer demonstrates the operation process through a virtual digital human and guides on-site personnel to perform maintenance through AR glasses.

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