Application of rough set theory in transformer fault diagnosis
Heming Li · Journal of North China Electric Power University · 2003
In order to improve diagnosis efficiency and compress the redundant features in the transformer fault diagnosis, the rough set theory is introduced and the feature reduction algorithm based on the rough set theory is presented. Based on the samples, the information table is constructed. By arranging different attribute set and calculating its classification quality, the minimal attribute set having the same classification quality with the whole attribute set can be obtained. The representative samples in transformer fault diagnosis are analyzed. The results show that the redundant features can be reduced and the main features which are more important to fault classification can be gotten.