High impact icing events pose a threat to the stable operation of power systems. However

under small-sample scenarios

traditional physics-knowledge guidance and data-driven prediction methods often have poor performance. Therefore

a physics knowledge guided iterative self-transfer learning (PKG-ISTL) model is proposed

integrating physics informed neural network (PINN) with self-transfer learning (STL). First

the data is divided into source and target domains

and a three-dimensional tensor containing spatial

feature

and temporal dimensions is constructed to achieve sliding-window-based icing prediction. Second

the model is built by combining PINN and STL. In the source domain branch

a PINN is trained to guide the model using physics knowledge. In the self-migration branch

multi-kernel maximum mean discrepancy is applied for domain adaptation. In the target domain branch

knowledge distillation is used to transfer physics knowledge from the expert model to the trained model. Finally

historical icing data from transmission lines in Guangxi province are used for simulation. Through interpretability analysis

the influence of meteorological

mechanical

line

and icing factors on line icing is revealed. Results show that compared to traditional data-driven models

the PKG-ISTL model improves prediction accuracy by 47.69%

verifying its effectiveness in small-sample scenarios.