Attention-Guided Multi-Task Learning for Fault Detection, Classification, and Localization in Power Transmission Systems

Timely and accurate fault diagnosis in power transmission systems is critical to ensuring grid stability, operational safety, and minimal service disruption. This study presents a unified deep learning framework that simultaneously performs fault identification, fault type classification, and fault location estimation using a multi-task learning (MTL) approach. Using the IEEE 39–Bus network, a comprehensive data set was generated under various load conditions, fault types, resistances, and location scenarios to reflect real-world variability. The proposed model integrates a shared representation layer and task-specific output heads, enhanced with an attention mechanism to dynamically prioritize salient input features. To further optimize the model architecture, Optuna was employed for hyperparameter tuning, enabling systematic exploration of design parameters such as neuron counts, dropout rates, activation functions, and learning rates. Experimental results demonstrate that the proposed Optimized Multi-Task Learning Attention Network (MTL-AttentionNet) achieves high accuracy across all three tasks, outperforming traditional models such as Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP), which require separate training for each task. The attention mechanism contributes to both interpretability and robustness, while the MTL design reduces computational redundancy. Overall, the proposed framework provides a unified and efficient solution for real-time fault diagnosis on the IEEE 39–bus transmission system, with promising implications for intelligent substation automation and smart grid resilience.