  Transmission line state assessment and prediction are of great significance
for the rational formulation of operation and maintenance strategy and
improvement of operation and maintenance level. Aiming at the problem that
existing models cannot take into account the robustness and data demand, this
paper proposes a state prediction method based on semi-supervised learning.
Firstly, for the expanded feature vector, the regular matrix is used to fill in
the missing data, and the sparse coding problem is solved by representation
learning. Then, with the help of a small number of labelled samples to
initially determine the category centers of line segments in different
defective states. Finally, the estimated parameters of the model are corrected
using unlabeled samples. Example analysis shows that this method can improve
the recognition accuracy and use data more efficiently than the existing
models.
