Forecast Overhead Transmission Lines Icing Based on Weighted Regression of Multiple Meteorological Factors

Accurate prediction of icing thickness on overhead transmission lines is crucial for ensuring the reliable operation of power transportation systems. In this study, we propose a novel approach based on weighted regression using multiple meteorological parameters to forecast icing thickness. The method leverages numerical modeling to obtain meteorological parameter predictions and employs statistical analysis to construct a predictive model. By integrating real-time monitoring data, our approach achieves a mean prediction error of approximately 0.5mm for icing thickness, outperforming the Makkonen model with an average error of around 1mm. The results demonstrate the effectiveness of our proposed method in accurately forecasting icing thickness. This research contributes to the advancement of scientific knowledge in the field of overhead line icing and offers valuable insights for optimizing prediction models and enhancing the resilience of power transmission systems.