Aiming at the current situation of high temperature of transformer components due to the closed environment and poor heat dissipation performance of box transformers, and the high accident rate of bushings, a box transformer based on Long Short-term Memory (LSTM) neural network is proposed. Prediction method of high voltage bushing temperature. First, analyze the heat flow of the high-voltage bushing of the box-type transformer. Then, a LSTM-based transformer high-voltage bushing temperature prediction model is established. The LSTM algorithm can effectively solve the problems of nonlinearity and time delay in the transformer high-voltage bushing temperature prediction. Finally, using infrared sensing technology to monitor the relevant data of the box-type transformer high-voltage bushing in a residential area, the field data is preprocessed, and the calculation example analysis is performed to verify that the proposed method has high prediction accuracy, small error and strong generalization ability. The comparison results show that the proposed method is better than ordinary recurrent neural network (RNN) and support vector machine (SVM) prediction methods. The average error is reduced by 27.4% and 36.3%, respectively, and the prediction accuracy is higher. It is more consistent with the measured value.
