Deep Learning for Fault Diagnosis in Power Transmission Lines: Current Trends, Limitations, and Future Directions

The resilience and efficiency of power transmission systems are essential to the stability of modern electrical grids, especially as these grids evolve with high penetrations of renewables and power electronics-based converters. Traditional fault detection and diagnosis (FDD) methods are increasingly challenged by the complexity, variability, and real-time demands of next-generation power networks. In this context, deep learning (DL) has emerged as a powerful tool, capable of extracting latent features from raw, high-dimensional data and offering robust, automated fault analysis. This paper surveys recent progress in DL-based fault diagnosis for transmission lines, with a focus on key neural architectures including convolutional neural networks (CNNs), recurrent networks (LSTM/GRU), attention mechanisms, and generative models. We explore how preprocessing, feature engineering, and data-driven model design impact diagnostic accuracy and generalization. The review also highlights hybrid approaches that combine signal processing with DL models, enabling enhanced interpretability and fault discrimination. Major research challenges such as limited labeled data, rare fault type detection, model robustness, and real-time deployment constraints are critically analyzed. Furthermore, we explore novel directions including self-supervised learning, transfer learning, edge deployment, and privacy-preserving federated learning tailored to smart grid ecosystems. This review aims to serve as a roadmap for advancing intelligent, scalable, and adaptive FDD systems in the era of digitalized power transmission networks.