AI Grant Abstract Polisher

This project proposes a novel graph neural network (GNN) framework for predictive maintenance in high-voltage distribution networks. By integrating time-series sensor data with topological grid constraints, the model identifies non-linear degradation patterns in transformers and switchgear. Our methodology employs self-supervised learning to mitigate the scarcity of labeled failure data in rural substations. This research aims to reduce unplanned outages by 35% through enhanced predictive fidelity, providing a robust, scalable architecture for localized smart grids. The resulting diagnostic engine provides a mathematical basis for automated intervention, ensuring grid stability against environmental stressors such as high-temperature flux and extreme weather events.