Transmission Line Icing Prediction Based on Physically Guided Fast-Slow Transformer

To improve the accuracy of the icing prediction model for overhead transmission lines, a physics-guided Fast-Slow Transformer icing prediction model for overhead transmission lines is proposed, which is based on the icing prediction model with meteorological input characteristics. First, the ice cover data is segmented into different time resolutions through Fourier transform; a transformer model based on Fourier transform is constructed to capture the local and global correlations of the ice cover data; then, according to the calculation model of the comprehensive load on the conductor and the conductor state equation, the variation law of ice thickness, temperature, wind speed, and tension is analyzed, and the model loss function is constructed according to the variation law to guide the training process of the model. Finally, the sample mixing enhancement algorithm is used to reduce the overfitting problem and improve the generalization performance of the prediction model. The results show that the proposed prediction model can consider the mechanical constraints in the ice growth process and accurately capture the dependence between ice cover and meteorology. Compared with traditional prediction models such as LSTM (Long Short-Term Memory) networks, its mean square error, mean absolute error, and mean absolute percentage error are reduced by 0.464–0.674, 0.41–0.53, and 8.87–11.5%, respectively, while the coefficient of determination (R2) is increased by 0.2–0.29.