Deep learning-based intelligent fault identification for power transmission lines

Abstract

Power transmission lines are essential components of power systems, and their operating conditions directly affect the safety, stability, and reliability of power supply. In practical operation, transmission lines are susceptible to various fault conditions, such as short circuits, grounding faults, conductor breakage, and insulator abnormalities, due to lightning strikes, strong winds, pollution, equipment aging, and external disturbances. Conventional fault identification methods mainly depend on relay protection signals, manual inspection, and model-based analysis. Although these methods have been widely applied, they often suffer from limited adaptability, insufficient feature representation capability, and reduced identification accuracy under complex operating conditions. To improve the accuracy and intelligence level of transmission line fault diagnosis, this paper proposes a deep learning-based intelligent fault identification method for power transmission lines. The proposed method employs a deep neural network to learn fault characteristics directly from transmission line monitoring data, thereby reducing dependence on manual feature extraction. Through hierarchical feature representation and nonlinear mapping, the method can effectively distinguish different fault patterns and improve fault classification performance. In addition, preprocessing and optimization strategies are introduced to suppress noise interference and enhance model robustness under imbalanced and complex data conditions. Experimental results show that the proposed method achieves better identification accuracy, stability, and generalization performance than conventional methods. The proposed method can provide effective technical support for online fault monitoring, rapid fault diagnosis, and intelligent operation and maintenance of transmission lines.