Attention-guided multi-task learning for fault detection in power systems #worldresearchawards


Introduction

Timely and accurate fault diagnosis is a cornerstone of modern power transmission system operation, directly influencing grid stability, safety, and service continuity. With increasing system complexity and penetration of intelligent devices, conventional single-task diagnostic approaches often fall short in meeting real-time and accuracy requirements. This research addresses these challenges by proposing a unified deep learning framework that integrates fault identification, fault type classification, and fault location estimation into a single multi-task learning paradigm, tailored for realistic transmission network conditions.

Multi-task learning framework for fault diagnosis

The proposed framework adopts a multi-task learning (MTL) architecture that enables simultaneous learning of multiple fault-related objectives within a shared representation space. By leveraging common features across tasks, the model reduces redundancy and improves generalization compared to training separate models. Task-specific output heads allow each diagnostic objective to retain specialization, ensuring that fault detection, classification, and localization are all performed efficiently within one coherent model.

Attention mechanism and feature prioritization

An attention mechanism is incorporated into the shared layers of the network to dynamically emphasize the most informative input features during training and inference. This mechanism allows the model to focus on critical signal patterns associated with fault events while suppressing irrelevant or noisy information. Beyond performance gains, attention weights also enhance model interpretability, offering insights into how different electrical measurements contribute to diagnostic decisions.

Data generation using the ieee 39–bus system

To ensure robustness and real-world relevance, a comprehensive dataset was generated using the IEEE 39–Bus transmission network. The dataset captures a wide range of operating conditions, including varying loads, fault types, fault resistances, and fault locations. This diversity enables the model to learn representative system behaviors and improves its ability to generalize across unseen scenarios commonly encountered in practical grid operations.

Hyperparameter optimization with optuna

The model architecture and training process were further refined through systematic hyperparameter optimization using Optuna. Key parameters such as neuron counts, activation functions, dropout rates, and learning rates were explored to identify optimal configurations. This automated tuning process significantly enhances model performance and stability, ensuring that the proposed MTL-AttentionNet operates at peak efficiency without reliance on manual trial-and-error design.

Performance evaluation and smart grid implications

Experimental results demonstrate that the optimized MTL-AttentionNet achieves high accuracy across all diagnostic tasks, consistently outperforming traditional methods such as SVM and MLP that require separate models for each function. The unified design reduces computational overhead while improving robustness and interpretability. These results highlight the framework’s strong potential for real-time deployment in intelligent substations, contributing to enhanced automation, faster fault response, and improved resilience in future smart grids.

Global Particle Physics Excellence Awards


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#worldresearchawards, #powertransmissionsystems, #faultdiagnosis, #multitasklearning, #deeplearning, #attentionmechanism, #smartgrid, #ieee39bus, #faultclassification, #faultlocation, #hyperparameteroptimization, #optuna, #gridstability, #intelligentsubstation, #powersystemprotection, #machinelearning, #realtimemonitoring, #energyresearch, #electricalengineering, #aiingrid, #researchinnovation,

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