AI application
HAI-FANG Real-Time Monitoring Solution for Power Grid Transformers and Towers
1、Overview
This solution leverages advanced CAE-AI hybrid modeling to enable high-precision real-time monitoring and early warnings for transformer temperature distribution, tower structural stress, and displacement. It overcomes limitations of traditional monitoring technologies, such as limited coverage and slow high-fidelity simulations, providing comprehensive, accurate, and rapid monitoring for power grid systems.
2、Implementation Approach
- Requirement Analysis: Collaborate with clients to define key parameters (e.g., transformer ambient temperature, input current, load rate; tower wind direction, load, ice accumulation).
- Data Collection: Gather high-fidelity simulation data to build training datasets.
- AI Model Development: Train machine learning algorithms to create 3D AI surrogate models for instant temperature field reconstruction and peak prediction.
- Real-Time Inference: Map input parameters to all monitoring nodes for rapid output.
- Visualization and Alerts: Render results via visualization software and trigger alarms for threshold-exceeding nodes.
3、Objectives
- High Accuracy: Achieve average errors of 5% for 120,000-node transformer temperature fields, and 0.3%/0.2% for tower displacement/stress across 3,000 nodes.
- Comprehensive Coverage: Monitor full temperature fields of transformers and entire tower structures.
- Visual Alerts: Provide intuitive visualization and timely warnings to ensure grid safety and stability.
