Prediction of Ice Accretion on Qinling Transmission Lines Integrating Image Recognition and Numerical Simulation

Objective Ice accretion on transmission lines is one of the common extreme meteorological risks in power systems. To enhance the safe operation of transmission lines, this study addresses key technical issues in ice accretion prediction and monitoring.

Methods A forecasting method for ice accumulation thickness was developed by integrating the numerical weather research and forecasting model (WRF) with the Makkonen physical icing model. Meanwhile, an innovative machine vision-based image recognition technique was introduced, proposing a non-contact measurement method for ice thickness based on pixel width analysis, enabling accurate validation of the model results.

Results A case study of an ice accretion event on transmission lines in the Qinling region, occurring on January 24, 2025, in Baoji City, revealed that the main meteorological background for the ice accretion was the synergistic mechanism of the formation of an inversion layer under the influence of upper- and lower-level atmospheric circulation patterns, combined with the transport of warm and moist air and orographic lifting; the icing thickness identification algorithm utilizing machine vision-based image recognition technique achieved an error rate of≤3.08%, meeting the accuracy requirements for practical transmission line monitoring and early warning, demonstrating significant potential for field application; the WRF model effectively simulated key meteorological elements during the icing process, with a correlation coefficient of 0.9 between simulated and observed values, indicating high reliability; the ice thickness simulated by the Makkonen model driven by WRF matched well with the field measurements from machine vision, with an average absolute error of only 0.632 mm, accurately reflecting the dynamic growth trend of ice accretion.

Conclusion The ice thickness prediction method based on the WRF model and Makkonen model, combined with machine vision-based image recognition technique, can effectively predict and monitor ice accretion thickness on transmission lines, providing a new technical approach to improve the safe operation of transmission lines.