Transformer State Prediction Based on Deep Learning Network

As a crucial component of the power system, transformers state directly impacts the stable operation of the system. Accurately predicting the oil temperature of transformers can effectively prevent faults and improve equipment reliability. To make the system more stable, this article uses long short-term memory networks (LSTM) to predict transformer oil temperature. A predictive model was constructed by collecting and analyzing historical operational data, including factors such as current, voltage, load, and ambient temperature. We do plenty of experiments and prove our model performs better in predict accuracy and response speed in oil temperature prediction, and can better capture the temporal characteristics of oil temperature changes. It provides new ideas for transformer condition monitoring and intelligent maintenance, laying the foundation for the intelligent development of the power industry.