Research on Winding Temperature Prediction of UHV Transformer Based on Convolutional Long Short-Term Memory Network

Abstract

In order to improve the prediction accuracy of ultra-high voltage (UHV) transformer winding temperature, a hybrid model based on convolutional neural network (CNN) and long short-term memory (LSTM) network is proposed. This method uses a large number of historical monitoring data, including data of winding temperature, top oil temperature, dissolved gas in oil, environmental data and so on. Combined with the characteristics of CNN and LSTM networks, CNN convolution layer and pooling layer are used to extract relevant data information to generate feature vectors, then the feature vectors are used as input data to predict winding temperature through LSTM network. The example results show that this method is of high prediction accuracy, its prediction accuracy is better than that of single LSTM model, which can effectively improve the prediction accuracy of UHV transformer winding temperature.KeywordsUHV transformerWinding temperatureDeep learningLong short-term memory network