An Improved Method for Rapid and Accurate Prediction of Transmission Line Icing

Ice coating on transmission lines is a significant factor threatening the safe and stable operation of power systems. Most existing ice coating prediction methods rely on complex signal preprocessing steps, such as Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and spectral clustering. This not only increases system complexity but may also introduce error accumulation, making it difficult to meet the requirements of practical engineering applications. This paper proposes an end-to-end transmission line ice coating prediction method based on an improved Transformer model. It learns the ice thickness prediction model directly from raw meteorological data, eliminating the need for complex signal decomposition and feature engineering. The proposed method significantly simplifies the system architecture, enhances the feasibility of engineering deployment, and provides a new technical approach for transmission line ice coating prediction, holding significant theoretical importance and practical value.