Objective To address the equipment failure and grid safety issues caused by ice accumulation on transmission lines, this study takes the lines crossing the Qinling Mountains in Baoji as the research object and conducts research on ice accumulation monitoring, prediction, and risk assessment technologies.

Method In the monitoring phase, machine vision technology was adopted. Through image preprocessing, conductor pixel extraction, and width measurement algorithms, combined with the "Ice Observation Spirit" image sensor, real-time monitoring of ice thickness was achieved. In the prediction phase, a database of surrounding meteorological stations was constructed. Based on the Haversine formula, meteorological data with high matching degrees were obtained. Then, the meteorological data were refined through interpolation methods. The processed data were input into the WRF numerical weather model to simulate key meteorological elements. The simulation results drove the Makkonen ice accumulation model to calculate the increase in ice thickness. Combined with real-time monitoring values, the prediction results were obtained. In the risk assessment phase, ice accumulation tripping samples, tower static data, and meteorological data were integrated. After feature engineering processing, an XGBoost model was constructed, and differentiated dynamic early warning thresholds were designed. Ultimately, the functions of the three modules were implemented based on Python, and a visualization monitoring, early warning, and risk assessment system was developed.

Result The research shows that the error between the ice thickness monitoring results and the manual measurement results is ≤ 3%, and the error of the ice thickness prediction results is ≤ 5%. In terms of risk assessment, it can accurately predict the probability of ice accumulation tripping within 1～3 hours and 3～6 hours and issue graded early warnings.

Conclusion The developed visualization monitoring, early warning, and risk assessment system can integrate monitoring, prediction, and assessment functions, providing decision support for the intelligent operation and maintenance of transmission lines in the Baoji power grid and effectively reducing the losses caused by ice accumulation disasters.