Big Data Analysis Based Transformer Temperature Prediction Method in Distribution Station Area

Abstract

The normal operation of power transformer is related to the safety and stability of the power grid. Abnormal temperature may cause damage to transformer equipment, seriously affect its service life, and even lead to major accidents. In this paper, a transformer temperature prediction method based on big data is proposed. The ambient temperature is included in the prediction conditions. A feature extraction method based on adaptive weighting is designed to mine the time series features in the column head temperature and ambient temperature, and an interactive feature fusion strategy is used to form a comprehensive and reliable transformer temperature prediction. The experimental simulation shows that the transformer temperature prediction method proposed in this paper has high prediction accuracy, effectively provides more quantitative auxiliary information for the operation monitoring of power transformer equipment, ensures the safe and stable operation of transformer, and has high practicability.