Transformer winding hot spot temperature prediction based on ϵ -fuzzy tree

Abstract

Transformer hot spot temperature is closely related to its operating life, which plays a key role in transformer thermal fault prevention and operational status monitoring. In order to effectively improve the prediction accuracy of the transformer winding hot spot temperature, a method based on ϵ-fuzzy tree (ϵ-FT) for predicting winding hot spot temperature is proposed. Taking the 220kV transformer of a substation as the research object, the input and output characteristic variables are extracted through the analysis of relevant mechanisms, which is applied to establish ϵ-FT model of the transformer winding hot spot temperature, and the proposed method is compared with the other methods. Subsequently, the noise and outliers are added to the modeling data to verify the robustness of the proposed method. The results show that the method can accurately predict the hot spot temperature and resist the bad data in the modeled samples, which has strong generalization ability and robustness.