Probability model for icing faults on overhead lines in distribution networks based on deep neural networks and attention mechanisms

To address the issue of power distribution system failures caused by ice accumulation on power lines, this paper combines the physical principles of ice accumulation on power lines and proposes a method for assessing the probability of ice accumulation-induced failures on overhead power lines in power distribution networks based on deep neural networks and attention mechanisms. The method uses DNN to deeply explore the relationship between ice thickness on power lines and weather factors in power distribution networks, and introduces the Transformer attention mechanism to enhance the model’s ability to extract important features, thereby improving the model’s robustness. Through case study analysis and comparison of the performance of the different artificial intelligence models proposed in this paper, the effectiveness and advanced nature of the proposed technology are validated. Finally, new directions for research on the calculation of ice accumulation fault probability in distribution lines are analyzed and discussed.