Transmission tower line fault prediction and analysis based on deep neural network

Under the dual pressure of continuous growth of global energy demand and environmental protection, ensuring efficient and stable operation of power transmission systems has become the top priority of governments and power departments around the world. As the backbone of power grids, the failure of transmission tower lines will not only significantly affect the continuity of power supply but also bring huge economic and social losses. In view of this, this paper proposes a transmission tower fault prediction and analysis framework based on deep neural network, which aims to realize the early warning of potential transmission tower line faults, provide guidance for the formulation of maintenance strategies, and improve the disaster prevention and mitigation ability of the power grid. In this study, we collected and sorted out multiple heterogeneous data, including operation logs, meteorological data, geological information, and power facility fault reports, to construct a comprehensive dataset. Through data preprocessing and feature engineering, we build deep learning models that include convolutional neural networks and recurrent neural networks to capture complex associations in spatial and temporal dimensions. Experimental results show that the model performs well in transmission tower line fault prediction, with an average accuracy of 94.7%, which is about 20% points higher than that of traditional statistical prediction methods. Especially under extreme weather conditions, the prediction accuracy of the model reached 96.5%, which fully demonstrated its robustness and applicability in specific scenarios, highlighting the novelty and unique contribution of this study.