Research on Transmission Line Icing Prediction for Power System Based on Improved Snake Optimization Algorithm-Optimized Deep Hybrid Kernel Extreme Learning Machine

Guanhua Li, Haoran Chen (), Shicong Sun, Tie Guo and Luyu Yang
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Guanhua Li: Electric Power Research Institute, Liaoning Electric Power Co., Ltd., State Grid, Shenyang 110006, China
Haoran Chen: School of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang 110866, China
Shicong Sun: Electric Power Research Institute, Liaoning Electric Power Co., Ltd., State Grid, Shenyang 110006, China
Tie Guo: Electric Power Research Institute, Liaoning Electric Power Co., Ltd., State Grid, Shenyang 110006, China
Luyu Yang: Electric Power Research Institute, Liaoning Electric Power Co., Ltd., State Grid, Shenyang 110006, China

Energies, 2025, vol. 18, issue 17, 1-24

Abstract: As extreme weather events become more frequent, the icing of transmission lines in winter has become more common, causing significant economic losses to power systems and drawing increasing attention. However, owing to the complexity of the conductor icing process, establishing high-precision ice thickness prediction models is vital for ensuring the safe and stable operation of power grids. Therefore, this paper proposes a hybrid model combining an improved snake optimization (ISO) algorithm, deep extreme learning machine (DELM), and hybrid kernel extreme learning machine (HKELM). Firstly, based on the analysis of the factors that influence the icing, the temperature, the humidity, the wind velocity, the wind direction, and the precipitation are selected as the weather parameters for the prediction model of the transmission line icing. Secondly, the HKELM is introduced into the regression layer of DELM to obtain the deep hybrid kernel extreme learning machine (DHKELM) model for ice thickness prediction. The SO algorithm is then augmented by incorporating the Latin hypercube sampling technique, t-distribution mutation strategy, and Cauchy mutation, enhancing its convergence. Finally, the ISO-DHKELM model is applied to the icing data of transmission lines in Sichuan Province for experiments. The simulation results indicate that this model not only performs well, but also enhances the accuracy of ice thickness predictions.

Keywords: ice thickness; transmission lines; grey association analysis; snake optimization algorithm; extreme learning machine (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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