Machine learning-based ice detection approach for power transmission lines by utilizing FBG micro-meteorological sensors.

Severe icing of transmission lines causes power failures, tower collapses, and other adverse events, which hinders the normal operation of society. Existing icing monitoring methods have problems of irregular monitoring and poor accuracy. In this study, we propose a comprehensive model for predicting hard rime and glaze ice using temperature, humidity, and historical icing data. The results of the experimental verification conducted for nine icing cycles in different years and geographic locations demonstrate that the proposed technique can effectively predict multiple types of icing while ensuring correlation coefficients > 0.99 and mean squared error < 4%.