Research on Lifecycle Management and Intelligent Maintenance Technologies for Oil-Immersed Transformers

This paper systematically investigates lifecycle management strategies for oil-immersed transformers from design and manufacturing to decommissioning and recycling, with particular focus on applications of next-generation information technologies including artificial intelligence and digital twins in intelligent maintenance. Research results demonstrate that adopting lifecycle management methods can extend transformer service life by 15-20%, while intelligent maintenance systems can reduce failure rates by over 30%.

1. Lifecycle Management Framework for Oil-Immersed Transformers

1.1 Key Technologies in Design Phase

• Reliability-Based Design (RBD) methodology
• Multi-physics coupling simulation of thermal-electrical-mechanical fields
• Selection and compatibility analysis of eco-friendly insulating oils

1.2 Quality Control in Manufacturing Phase

• Digital monitoring system for critical processes
• Online winding deformation detection technology
• Optimization of vacuum oil filling parameters

2. Intelligent Monitoring and Fault Diagnosis Technologies

2.1 Multi-Parameter Integrated Monitoring System

2.2 Deep Learning-Based Fault Diagnosis

• Convolutional Neural Networks (CNN) for partial discharge pattern recognition
• Long Short-Term Memory (LSTM) networks for insulation aging trend prediction
• Achieved 92.3% fault diagnosis accuracy

3. Application of Digital Twin Technology in Maintenance

3.1 Twin Model Construction

• 3D electromagnetic-thermal-mechanical coupled model
• Real-time data-driven model updating mechanism
• Virtual sensor technology to fill monitoring gaps

3.2 Typical Application Scenarios

• Dynamic load capacity assessment
• Maintenance strategy optimization
• Emergency drill simulation

4. Life Extension and Decommissioning Technologies

4.1 Aging Assessment Methods

• Insulation condition evaluation based on frequency domain dielectric spectroscopy
• Residual life prediction model for mechanical strength
• Comprehensive economic evaluation system

4.2 Environmentally Friendly Disposal Technologies

• Vacuum distillation regeneration process for insulating oil
• High-efficiency copper winding separation and recovery
• Silicon steel sheet magnetic property restoration

5. Architecture of Intelligent Maintenance System

5.1 System Composition

text

[Sensing Layer] --5G--> [Edge Computing Layer] --Fiber--> [Cloud Platform]
    ↑                     ↑                        ↑
Sensor Array        Intelligent Diagnosis Node    Big Data Analytics Center

5.2 Functional Modules

• Condition assessment module
• Risk early warning module
• Decision support module
• Knowledge management module

6. Engineering Application Case Study

6.1 Implementation Results at 500kV Substation

• Fault warning accuracy: 89.7%
• Maintenance cost reduction: 28.5%
• Unplanned outage reduction: 42.3%

7. Future Research Directions

• Application of quantum sensing technology in condition monitoring
• Blockchain-based supply chain management system
• Development of self-healing insulation materials

Conclusion

The lifecycle management methodology and intelligent maintenance technology system proposed in this research can significantly improve the operational reliability and cost-effectiveness of oil-immersed transformers, providing crucial technical support for smart grid construction.