A metaheuristic-optimization-based neural network for icing prediction on transmission lines

Ice accretion on overhead transmission line systems is a leading cause of power outages and can lead to substantial economic losses in northern regions. Therefore, accurately and rapidly predicting ice accretion on power lines is crucial for ensuring the safe operation of the power grid. This study introduces a machine learning method for predicting the ice-to-liquid ratio ($\mathrm{ILR}$), an important parameter for assessing ice accretion efficiency. While estimating $\mathrm{ILR}$ is vital for operational forecasting, many existing ice accretion models do not include this capability. A feedforward neural network (FFNN) trained with stochastic gradient descent and various metaheuristic optimizers - specifically particle swarm optimization, grey wolf optimizer, whale optimizer, and slime mold optimizer - is employed to forecast hourly $\mathrm{ILR}$. Environmental data required for training and testing the FFNN model were obtained from the Automated Surface Observing System (ASOS). A global sensitivity analysis using the Sobol index, evaluated via the coefficients of a polynomial chaos expansion, was conducted to identify the most influential input parameters. The results indicate that only four input parameters significantly contribute to the variance in the response: precipitation, temperature, dew point temperature, and wind speed. Furthermore, the FFNN model trained with metaheuristic optimizers outperformed the stochastic gradient descent approach. With the predicted $\mathrm{ILR}$, ice accumulation can be easily calculated as the product of $\mathrm{ILR}$ and the amount of liquid precipitation depth.