Multi-Task Learning for Integrated Fault Analysis in Power System Protection

Modern electric power systems require rapid and reliable fault analysis to ensure grid stability amid increasing renewable integration. This project explores multi-task learning as a unified framework for simultaneously detecting, classifying, and localizing faults in transmission networks. By sharing representations across tasks, the model aims to reduce redundancy and enhance generalization compared to traditional single-task approaches. The results will contribute to the development of scalable, data-driven protection schemes for future intelligent power grids.